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font="default" size="100%">n/a</style></abstract><notes><style face="normal" font="default" size="100%">n/a</style></notes></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, Frederico</style></author><author><style face="normal" font="default" size="100%">Cabral,Ivanilda</style></author><author><style face="normal" font="default" size="100%">Gomes, M. Ivette</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Improving Asymptotically Unbiased Extreme Value Index Estimation</style></title><secondary-title><style face="normal" font="default" size="100%">Contributions to Statistics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1007%2F978-3-319-76605-8_11</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer International Publishing</style></publisher><pages><style face="normal" font="default" size="100%">155–163</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">n/a</style></abstract><notes><style face="normal" font="default" size="100%">n/a</style></notes></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro,Frederico Almeida Gião Gonçalves</style></author><author><style face="normal" font="default" size="100%">Gomes, M. Ivette</style></author><author><style face="normal" font="default" size="100%">Henriques-Rodrigues,Lígia</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A location-invariant probability weighted moment estimation of the Extreme Value Index</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Computer Mathematics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">adaptive semi-parametric estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">Asymptotic properties</style></keyword><keyword><style  face="normal" font="default" size="100%">Bootstrap methodology</style></keyword><keyword><style  face="normal" font="default" size="100%">extreme value index</style></keyword><keyword><style  face="normal" font="default" size="100%">heavy tails</style></keyword><keyword><style  face="normal" font="default" size="100%">location/scale-invariant estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">Monte-Carlo simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistics of extremes</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016/4/2</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.scopus.com/inward/record.url?scp=84909952827&amp;partnerID=8YFLogxK</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">93</style></volume><pages><style face="normal" font="default" size="100%">676 - 695</style></pages><isbn><style face="normal" font="default" size="100%">0020-7160</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The peaks over random threshold (PORT) methodology and the Pareto probability weighted moments (PPWM) of the largest observations are used to build a class of location-invariant estimators of the Extreme Value Index (EVI), the primary parameter in statistics of extremes. The asymptotic behaviour of such a class of EVI-estimators, the so-called PORT-PPWM EVI-estimators, is derived, and an alternative class of location-invariant EVI-estimators, the generalized Pareto probability weighted moments (GPPWM) EVI-estimators is considered as an alternative. These two classes of estimators, the PORT-PPWM and the GPPWM, jointly with the classical Hill EVI-estimator and a recent class of minimum-variance reduced-bias estimators are compared for finite samples, through a large-scale Monte-Carlo simulation study. An adaptive choice of the tuning parameters under play is put forward and applied to simulated and real data sets.The peaks over random threshold (PORT) methodology and the Pareto probability weighted moments (PPWM) of the largest observations are used to build a class of location-invariant estimators of the Extreme Value Index (EVI), the primary parameter in statistics of extremes. The asymptotic behaviour of such a class of EVI-estimators, the so-called PORT-PPWM EVI-estimators, is derived, and an alternative class of location-invariant EVI-estimators, the generalized Pareto probability weighted moments (GPPWM) EVI-estimators is considered as an alternative. These two classes of estimators, the PORT-PPWM and the GPPWM, jointly with the classical Hill EVI-estimator and a recent class of minimum-variance reduced-bias estimators are compared for finite samples, through a large-scale Monte-Carlo simulation study. An adaptive choice of the tuning parameters under play is put forward and applied to simulated and real data sets.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue><notes><style face="normal" font="default" size="100%">&lt;p&gt;Fundacao para a Ciencia e a Tecnologia (PEst-OE/MAT/UI0006/2014PEst-OE/MAT/UI0297/2014)&lt;br /&gt;
PTDC/FEDER (SFRH/BPD/77319/2011)&lt;/p&gt;
</style></notes><custom2><style face="normal" font="default" size="100%">10.1080/00207160.2014.975217</style></custom2></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, Frederico</style></author><author><style face="normal" font="default" size="100%">Marques,Filipe J.</style></author><author><style face="normal" font="default" size="100%">Mateus,Ayana</style></author><author><style face="normal" font="default" size="100%">Atal,Serra</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A note on the Jackson exponentiality test</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference of Computational Methods in Sciences and Engineering 2016, ICCMSE 2016</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computer Science</style></keyword><keyword><style  face="normal" font="default" size="100%">Physics, Applied</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016/12/6</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.scopus.com/inward/record.url?scp=85008681209&amp;partnerID=8YFLogxK</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">American Institute of Physics Inc.</style></publisher><volume><style face="normal" font="default" size="100%">1790</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In this paper we revisit the Jackson exponentiality test. We study and provide functions in R language to compute theoretical moments, the distribution function and quantiles of the statistic test. Approximations to the exact distribution function and quantiles are also provided and their precision discussed. In addition, we provide an application of the Jackson test to real data.In this paper we revisit the Jackson exponentiality test. We study and provide functions in R language to compute theoretical moments, the distribution function and quantiles of the statistic test. Approximations to the exact distribution function and quantiles are also provided and their precision discussed. In addition, we provide an application of the Jackson test to real data.&lt;/p&gt;
</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;sem pdf conforme despacho&lt;/p&gt;
</style></notes><custom2><style face="normal" font="default" size="100%">10.1063/1.4968686</style></custom2></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cabral,Ivanilda</style></author><author><style face="normal" font="default" size="100%">Caeiro, Frederico</style></author><author><style face="normal" font="default" size="100%">Gomes, M. Ivette</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Reduced bias Hill estimators</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference of Computational Methods in Sciences and Engineering 2016, ICCMSE 2016</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computer Science</style></keyword><keyword><style  face="normal" font="default" size="100%">Physics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016/12/6</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.scopus.com/inward/record.url?scp=85008697798&amp;partnerID=8YFLogxK</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">American Institute of Physics Inc.</style></publisher><volume><style face="normal" font="default" size="100%">1790</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;For heavy tails, classical extreme value index estimators, like the Hill estimator, are usually asymptotically biased. Consequently those estimators are quite sensitive to the number of top order statistics used in the estimation. The recent minimum-variance reduced-bias extreme value index estimators enable us to remove the dominant component of asymptotic bias and keep the asymptotic variance of the new estimators equal to the asymptotic variance of the Hill estimator. In this paper a new minimum-variance reduced-bias extreme value index estimator is introduced, and its non degenerate asymptotic behaviour is studied. A comparison with another important minimum-variance reduced-bias extreme value index estimator is also provided.For heavy tails, classical extreme value index estimators, like the Hill estimator, are usually asymptotically biased. Consequently those estimators are quite sensitive to the number of top order statistics used in the estimation. The recent minimum-variance reduced-bias extreme value index estimators enable us to remove the dominant component of asymptotic bias and keep the asymptotic variance of the new estimators equal to the asymptotic variance of the Hill estimator. In this paper a new minimum-variance reduced-bias extreme value index estimator is introduced, and its non degenerate asymptotic behaviour is studied. A comparison with another important minimum-variance reduced-bias extreme value index estimator is also provided.&lt;/p&gt;
</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;sem pdf conforme despacho&lt;/p&gt;
</style></notes><custom2><style face="normal" font="default" size="100%">10.1063/1.4968687</style></custom2></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mateus,Ayana</style></author><author><style face="normal" font="default" size="100%">Caeiro, Frederico</style></author><author><style face="normal" font="default" size="100%">Gomes,Dora Prata</style></author><author><style face="normal" font="default" size="100%">Sequeira,Inês J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Statistical analysis of extreme river flows</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference of Computational Methods in Sciences and Engineering 2016, ICCMSE 2016</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">AIP Conference Proceedings</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">INFERENCE</style></keyword><keyword><style  face="normal" font="default" size="100%">TESTS</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016/12/6</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.scopus.com/inward/record.url?scp=85008656018&amp;partnerID=8YFLogxK</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">American Institute of Physics Inc.</style></publisher><volume><style face="normal" font="default" size="100%">1790</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Floods are recurrent events that can have a catastrophic impact. In this work we are interested in the analysis of a data set of gauged daily flows from the Whiteadder Water river, Scotland. Using statistic techniques based on extreme value theory, we estimate several extreme value parameters, including extreme quantiles and return periods of high levels.Floods are recurrent events that can have a catastrophic impact. In this work we are interested in the analysis of a data set of gauged daily flows from the Whiteadder Water river, Scotland. Using statistic techniques based on extreme value theory, we estimate several extreme value parameters, including extreme quantiles and return periods of high levels.&lt;/p&gt;
</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;Sem PDF.&lt;/p&gt;
</style></notes><custom2><style face="normal" font="default" size="100%">10.1063/1.4968685</style></custom2></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, Frederico</style></author><author><style face="normal" font="default" size="100%">Gomes, M. Ivette</style></author><author><style face="normal" font="default" size="100%">Beirlant,Jan</style></author><author><style face="normal" font="default" size="100%">de Wet,Tertius</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mean-of-order p reduced-bias extreme value index estimation under a third-order framework</style></title><secondary-title><style face="normal" font="default" size="100%">ExtremesExtremes</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">bias estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">heavy tails</style></keyword><keyword><style  face="normal" font="default" size="100%">Optimal levels</style></keyword><keyword><style  face="normal" font="default" size="100%">Semi-parametric reduced-bias estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistics of extremes</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016/12/1</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.scopus.com/inward/record.url?scp=84975457832&amp;partnerID=8YFLogxK</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">19</style></volume><pages><style face="normal" font="default" size="100%">561 - 589</style></pages><isbn><style face="normal" font="default" size="100%">1386-1999</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Reduced-bias versions of a very simple generalization of the ‘classical’ Hill estimator of a positive extreme value index (EVI) are put forward. The Hill estimator can be regarded as the logarithm of the mean-of-order-0 of a certain set of statistics. Instead of such a geometric mean, it is sensible to consider the mean-of-order-p (MOP) of those statistics, with p real. Under a third-order framework, the asymptotic behaviour of the MOP, optimal MOP and associated reduced-bias classes of EVI-estimators is derived. Information on the dominant non-null asymptotic bias is also provided so that we can deal with an asymptotic comparison at optimal levels of some of those classes. Large-scale Monte-Carlo simulation experiments are undertaken to provide finite sample comparisons.Reduced-bias versions of a very simple generalization of the ‘classical’ Hill estimator of a positive extreme value index (EVI) are put forward. The Hill estimator can be regarded as the logarithm of the mean-of-order-0 of a certain set of statistics. Instead of such a geometric mean, it is sensible to consider the mean-of-order-p (MOP) of those statistics, with p real. Under a third-order framework, the asymptotic behaviour of the MOP, optimal MOP and associated reduced-bias classes of EVI-estimators is derived. Information on the dominant non-null asymptotic bias is also provided so that we can deal with an asymptotic comparison at optimal levels of some of those classes. Large-scale Monte-Carlo simulation experiments are undertaken to provide finite sample comparisons.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue><notes><style face="normal" font="default" size="100%">&lt;p&gt;Fundacao para a Ciencia e a Tecnologia (FCT) - UID/MAT/00006/2013 (CEA/UL) ; UID/MAT/00297/2013 (CMA/UNL)COST Action -  IC1408&lt;br /&gt;
KU Leuven project - HSTRT/14/001 &lt;/p&gt;
</style></notes><custom2><style face="normal" font="default" size="100%">10.1007/s10687-016-0261-5</style></custom2></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cabral,Ivanilda</style></author><author><style face="normal" font="default" size="100%">Caeiro, Frederico</style></author><author><style face="normal" font="default" size="100%">Gomes, M. Ivette</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Redução do viés do estimador de Hill: uma nova abordagem</style></title><secondary-title><style face="normal" font="default" size="100%">Estatística: Progressos e Aplicações</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016/11</style></date></pub-dates></dates><pages><style face="normal" font="default" size="100%">73 - 84</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">n/a</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;Sem PDF.&lt;/p&gt;
</style></notes></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gomes, M. Ivette</style></author><author><style face="normal" font="default" size="100%">Caeiro, Frederico</style></author><author><style face="normal" font="default" size="100%">Henriques-Rodrigues,Lígia</style></author><author><style face="normal" font="default" size="100%">Manjunath,B.g.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bootstrap Methods in Statistics of Extremes</style></title><secondary-title><style face="normal" font="default" size="100%">Extreme Events in Finance</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016/10/7</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">John Wiley &amp; Sons, Inc.</style></publisher><pages><style face="normal" font="default" size="100%">117 - 138</style></pages><isbn><style face="normal" font="default" size="100%">9781118650196</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">n/a</style></abstract><notes><style face="normal" font="default" size="100%">n/a</style></notes><custom2><style face="normal" font="default" size="100%">10.1002/9781118650318.ch6</style></custom2></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, Frederico</style></author><author><style face="normal" font="default" size="100%">Gomes, M. Ivette</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Threshold Selection in Extreme Value Analysis</style></title><secondary-title><style face="normal" font="default" size="100%">Extreme Value Modeling and Risk Analysis</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Bootstrap methodology</style></keyword><keyword><style  face="normal" font="default" size="100%">Heuristic methods</style></keyword><keyword><style  face="normal" font="default" size="100%">Optimal sample fraction</style></keyword><keyword><style  face="normal" font="default" size="100%">Sample-paths stability</style></keyword><keyword><style  face="normal" font="default" size="100%">semi-parametric estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistical theory of extremes</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016/1/7</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Chapman and Hall/CRC 2007</style></publisher><pages><style face="normal" font="default" size="100%">69 - 86</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4987-0129-7</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The main objective of statistics of extremes is the prediction of rare events, and its primary problem has been the estimation of the extreme value index (EVI). Whenever we are interested in large values, such estimation is usually performed on the basis of the largest k + 1 order statistics in the sample or on the excesses over a high level u. The question that has been often addressed in practical applications of extreme value theory is the choice of either k or u, and an adaptive EVI-estimation. Such a choice can be either heuristic or based on sample paths stability or on the minimization of a mean squared error estimateas a function of k. Some of these procedures will be reviewed. Despite of thefact that the methods provided can be applied, with adequate modifications, to any real EVI and not only to the adaptive EVI-estimation but also to the adaptive estimation of other relevant right-tail parameters, we shall illustrate the methods essentially for the EVI and for heavy tails, i.e., for a positive EVI.The main objective of statistics of extremes is the prediction of rare events, and its primary problem has been the estimation of the extreme value index (EVI). Whenever we are interested in large values, such estimation is usually performed on the basis of the largest k + 1 order statistics in the sample or on the excesses over a high level u. The question that has been often addressed in practical applications of extreme value theory is the choice of either k or u, and an adaptive EVI-estimation. Such a choice can be either heuristic or based on sample paths stability or on the minimization of a mean squared error estimateas a function of k. Some of these procedures will be reviewed. Despite of thefact that the methods provided can be applied, with adequate modifications, to any real EVI and not only to the adaptive EVI-estimation but also to the adaptive estimation of other relevant right-tail parameters, we shall illustrate the methods essentially for the EVI and for heavy tails, i.e., for a positive EVI.&lt;/p&gt;
</style></abstract><notes><style face="normal" font="default" size="100%">n/a</style></notes><custom2><style face="normal" font="default" size="100%">10.1201/b19721-5</style></custom2></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, Frederico</style></author><author><style face="normal" font="default" size="100%">Martins,Ana P.</style></author><author><style face="normal" font="default" size="100%">Sequeira,Inês J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Finite sample behaviour of classical and quantile regression estimators for the Pareto distribution</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the International Conference on Numerical Analysis and Applied Mathematics 2014, ICNAAM 2014</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Maximum likelihood estimator</style></keyword><keyword><style  face="normal" font="default" size="100%">Moment estimator</style></keyword><keyword><style  face="normal" font="default" size="100%">Monte-Carlo method</style></keyword><keyword><style  face="normal" font="default" size="100%">Pareto distribution</style></keyword><keyword><style  face="normal" font="default" size="100%">Quantile regression estimator</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015/3/10</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.scopus.com/inward/record.url?scp=84939648344&amp;partnerID=8YFLogxK</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">American Institute of Physics Inc.</style></publisher><volume><style face="normal" font="default" size="100%">1648</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The Pareto distribution is a well known and important model in Statistics. It has been used to study large incomes, city population size, size of losses, stock price fluctuations, number of citations received by papers and other similar phenomena. In this work we compare the finite sample performance of several estimation methods, namely the Moment, Maximum Likelihood and Quantile Regression methods. The comparison will be made through a Monte-Carlo simulation study.The Pareto distribution is a well known and important model in Statistics. It has been used to study large incomes, city population size, size of losses, stock price fluctuations, number of citations received by papers and other similar phenomena. In this work we compare the finite sample performance of several estimation methods, namely the Moment, Maximum Likelihood and Quantile Regression methods. The comparison will be made through a Monte-Carlo simulation study.&lt;/p&gt;
</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;sem pdf conforme despacho&lt;/p&gt;
</style></notes><custom2><style face="normal" font="default" size="100%">10.1063/1.4912753</style></custom2></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, Frederico</style></author><author><style face="normal" font="default" size="100%">Gomes,Dora Susana Raposo Prata</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Adaptive estimation of a tail shape second order parameter</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference of Computational Methods in Sciences and Engineering 2015 (ICCMSE 2015)</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">AIP Conference Proceedings</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Asymptotic properties</style></keyword><keyword><style  face="normal" font="default" size="100%">Heavy tail</style></keyword><keyword><style  face="normal" font="default" size="100%">Monte Carlo simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Shape second-order parameter</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistics of extremes</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015/12/31</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.scopus.com/inward/record.url?scp=84984535413&amp;partnerID=8YFLogxK</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">American Institute of Physics Inc.</style></publisher><isbn><style face="normal" font="default" size="100%">978-073541349-8</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In Statistics of Extremes, the tail shape second order parameter is a relevant parameter whenever we want to improve the estimation of first order parameters. We shall consider two semi-parametric estimators of the shape second order parameter, parameterized with a tuning parameter. We provide a Monte Carlo comparative simulation study of several algorithms for the choice of such tuning parameter and for an adaptive estimation of the shape second order parameter.In Statistics of Extremes, the tail shape second order parameter is a relevant parameter whenever we want to improve the estimation of first order parameters. We shall consider two semi-parametric estimators of the shape second order parameter, parameterized with a tuning parameter. We provide a Monte Carlo comparative simulation study of several algorithms for the choice of such tuning parameter and for an adaptive estimation of the shape second order parameter.&lt;/p&gt;
</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;Sem PDF.This work was partially supported by the Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) through the project UID/MAT/00297/2013 (Centro de Matemática e Aplicações).&lt;/p&gt;
</style></notes><custom2><style face="normal" font="default" size="100%">10.1063/1.4938771</style></custom2></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, Frederico</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Preface of the &quot;2nd Symposium on Computational Statistical Methods&quot;</style></title><secondary-title><style face="normal" font="default" size="100%">AIP Conference ProceedingsAIP Conference Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015/12/31</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.scopus.com/inward/record.url?scp=84984545957&amp;partnerID=8YFLogxK</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">1702</style></volume><isbn><style face="normal" font="default" size="100%">0094-243X</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">n/a</style></abstract><notes><style face="normal" font="default" size="100%">n/a</style></notes><custom2><style face="normal" font="default" size="100%">10.1063/1.4938767</style></custom2></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, Frederico</style></author><author><style face="normal" font="default" size="100%">Gomes, M. Ivette</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Revisiting the maximum likelihood estimation of a positive extreme value index</style></title><secondary-title><style face="normal" font="default" size="100%">Journal Of Statistical Theory And PracticeJournal Of Statistical Theory And Practice</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">bias estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">heavy tails</style></keyword><keyword><style  face="normal" font="default" size="100%">Semiparametric estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistics of extremes</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015/1/13</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.scopus.com/inward/record.url?scp=84909966167&amp;partnerID=8YFLogxK</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">200 - 218</style></pages><isbn><style face="normal" font="default" size="100%">1559-8608</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In this article, we revisit Feuerverger and Halls maximum likelihood estimation of the extreme value index. Based on those estimators we propose new estimators that have the smallest possible asymptotic variance, equal to the asymptotic variance of the Hill estimator. The full asymptotic distributional properties of the estimators are derived under a general third-order framework for heavy tails. Applications to a real data set and to simulated data are also presented.In this article, we revisit Feuerverger and Halls maximum likelihood estimation of the extreme value index. Based on those estimators we propose new estimators that have the smallest possible asymptotic variance, equal to the asymptotic variance of the Hill estimator. The full asymptotic distributional properties of the estimators are derived under a general third-order framework for heavy tails. Applications to a real data set and to simulated data are also presented.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><notes><style face="normal" font="default" size="100%">&lt;p&gt;Sem PDF.&lt;/p&gt;
</style></notes><custom2><style face="normal" font="default" size="100%">10.1080/15598608.2014.909754</style></custom2></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, Frederico</style></author><author><style face="normal" font="default" size="100%">Gomes, M. Ivette</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bias reduction in the estimation of a shape second-order parameter of a heavy-tailed model</style></title><secondary-title><style face="normal" font="default" size="100%">Journal Of Statistical Computation And SimulationJournal Of Statistical Computation And Simulation</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">bias reduction</style></keyword><keyword><style  face="normal" font="default" size="100%">heavy tails</style></keyword><keyword><style  face="normal" font="default" size="100%">second-order parameter</style></keyword><keyword><style  face="normal" font="default" size="100%">semi-parametric estimation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.scopus.com/inward/record.url?scp=84941740831&amp;partnerID=8YFLogxK</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">85</style></volume><pages><style face="normal" font="default" size="100%">3405 - 3419</style></pages><isbn><style face="normal" font="default" size="100%">0094-9655</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In extreme value theory, the shape second-order parameter is a quite relevant parameter related to the speed of convergence of maximum values, linearly normalized, towards its limit law. The adequate estimation of this parameter is vital for improving the estimation of the extreme value index, the primary parameter in statistics of extremes. In this article, we consider a recent class of semi-parametric estimators of the shape second-order parameter for heavy right-tailed models. These estimators, based on the largest order statistics, depend on a real tuning parameter, which makes them highly flexible and possibly unbiased for several underlying models. In this article, we are interested in the adaptive choice of such tuning parameter and the number of top order statistics used in the estimation procedure. The performance of the methodology for the adaptive choice of parameters is evaluated through a Monte Carlo simulation study.In extreme value theory, the shape second-order parameter is a quite relevant parameter related to the speed of convergence of maximum values, linearly normalized, towards its limit law. The adequate estimation of this parameter is vital for improving the estimation of the extreme value index, the primary parameter in statistics of extremes. In this article, we consider a recent class of semi-parametric estimators of the shape second-order parameter for heavy right-tailed models. These estimators, based on the largest order statistics, depend on a real tuning parameter, which makes them highly flexible and possibly unbiased for several underlying models. In this article, we are interested in the adaptive choice of such tuning parameter and the number of top order statistics used in the estimation procedure. The performance of the methodology for the adaptive choice of parameters is evaluated through a Monte Carlo simulation study.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">17</style></issue><notes><style face="normal" font="default" size="100%">&lt;p&gt;Sem PDF.Fundacao para a Ciencia e a Tecnologia (PEst-OE/MAT/UI0006/2014; PEst-OE/MAT/UI0297/2014)&lt;/p&gt;
</style></notes><custom2><style face="normal" font="default" size="100%">10.1080/00949655.2014.975707</style></custom2></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mateus,Ayana Maria Xavier Furtado</style></author><author><style face="normal" font="default" size="100%">Caeiro,Frederico Almeida Gião Gonçalves</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The difference-sign randomness test</style></title><secondary-title><style face="normal" font="default" size="100%">NTERNATIONAL CONFERENCE OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2015</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">AIP Conference Proceedings</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Difference sign</style></keyword><keyword><style  face="normal" font="default" size="100%">Non-parametric inference</style></keyword><keyword><style  face="normal" font="default" size="100%">randomness test</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">American Institute of Physics Inc.</style></publisher><volume><style face="normal" font="default" size="100%">1702</style></volume><isbn><style face="normal" font="default" size="100%">978-0-7354-1349-8</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In this paper we review the properties of the difference-sign randomness test. First we analyse the exact andasymptotic distribution of the test statistic and provide a table with values for the exact distribution function, for samples ofsize n ≤ 32. Then, we also present several moments of the statistic test, under the null hypothesis of randomness and underthe hypothesis of the existence of a linear trend. Finally, we present an illustration of the test difference-sign to a real data set.In this paper we review the properties of the difference-sign randomness test. First we analyse the exact andasymptotic distribution of the test statistic and provide a table with values for the exact distribution function, for samples ofsize n ≤ 32. Then, we also present several moments of the statistic test, under the null hypothesis of randomness and underthe hypothesis of the existence of a linear trend. Finally, we present an illustration of the test difference-sign to a real data set.&lt;/p&gt;
</style></abstract><notes><style face="normal" font="default" size="100%">n/a</style></notes><custom2><style face="normal" font="default" size="100%">10.1063/1.4938768</style></custom2></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro,Frederico Almeida Gião Gonçalves</style></author><author><style face="normal" font="default" size="100%">Mateus,Ayana Maria Xavier Furtado</style></author><author><style face="normal" font="default" size="100%">Ramos,Luís Pedro Carneiro</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Extreme value analysis of the sea levels in Venice</style></title><secondary-title><style face="normal" font="default" size="100%">PROCEEDINGS OF THE INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2014</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">AIP Conference Proceedings</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Extreme quantile</style></keyword><keyword><style  face="normal" font="default" size="100%">extreme value index</style></keyword><keyword><style  face="normal" font="default" size="100%">retun level</style></keyword><keyword><style  face="normal" font="default" size="100%">sea level</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistics of extremes</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">American Institute of Physics Inc.</style></publisher><isbn><style face="normal" font="default" size="100%">978-0-7354-1287-3</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The number of floods in the city of Venice has increased substantially in the last decades and can be explained bythe sea level rise and land subsidence. Using Statistics of Extremes we shall model the extreme behaviour of the sea level inVenice and quantify risk through the estimation of important parameters such as return periods of high levels.The number of floods in the city of Venice has increased substantially in the last decades and can be explained bythe sea level rise and land subsidence. Using Statistics of Extremes we shall model the extreme behaviour of the sea level inVenice and quantify risk through the estimation of important parameters such as return periods of high levels.&lt;/p&gt;
</style></abstract><notes><style face="normal" font="default" size="100%">n/a</style></notes><custom2><style face="normal" font="default" size="100%">10.1063/1.4912752</style></custom2></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, Frederico</style></author><author><style face="normal" font="default" size="100%">Gomes,Dora Susana Raposo Prata</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A log probability weighted moment estimator of extreme quantiles</style></title><secondary-title><style face="normal" font="default" size="100%">Theory and Practice of Risk Assessment - ICRA5 2013</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Extreme quantile</style></keyword><keyword><style  face="normal" font="default" size="100%">extreme value index</style></keyword><keyword><style  face="normal" font="default" size="100%">Log probability weighted moment</style></keyword><keyword><style  face="normal" font="default" size="100%">Optimal level</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistics of extremes</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.scopus.com/inward/record.url?scp=84946404285&amp;partnerID=8YFLogxK</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer New York LLC</style></publisher><volume><style face="normal" font="default" size="100%">136</style></volume><pages><style face="normal" font="default" size="100%">293 - 303</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In this paper we consider the semi-parametric estimation of extreme quantiles of a right heavy-tail model. We propose a new Probability Weighted Moment estimator for extreme quantiles, which is obtained from the estimators of the shape and scale parameters of the tail. Under a second-order regular variation condition on the tail, of the underlying distribution function, we deduce the non degenerate asymptotic behaviour of the estimators under study and present an asymptotic comparison at their optimal levels. In addition, the performance of the estimators is illustrated through an application to real data.In this paper we consider the semi-parametric estimation of extreme quantiles of a right heavy-tail model. We propose a new Probability Weighted Moment estimator for extreme quantiles, which is obtained from the estimators of the shape and scale parameters of the tail. Under a second-order regular variation condition on the tail, of the underlying distribution function, we deduce the non degenerate asymptotic behaviour of the estimators under study and present an asymptotic comparison at their optimal levels. In addition, the performance of the estimators is illustrated through an application to real data.&lt;/p&gt;
</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;sem pdf conforme despacho&lt;/p&gt;
</style></notes><custom2><style face="normal" font="default" size="100%">10.1007/978-3-319-18029-8_22</style></custom2></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gomes, M. Ivette</style></author><author><style face="normal" font="default" size="100%">Brilhante,M. Fátima</style></author><author><style face="normal" font="default" size="100%">Caeiro, Frederico</style></author><author><style face="normal" font="default" size="100%">Pestana, Dinis</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A new partially reduced-bias mean-of-order p class of extreme value index estimators</style></title><secondary-title><style face="normal" font="default" size="100%">Computational Statistics &amp; Data AnalysisComputational Statistics &amp; Data Analysis</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">bias estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">Heavy right-tails</style></keyword><keyword><style  face="normal" font="default" size="100%">Heuristic methods</style></keyword><keyword><style  face="normal" font="default" size="100%">Optimal levels</style></keyword><keyword><style  face="normal" font="default" size="100%">semi-parametric estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">Statistics of extremes</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.scopus.com/inward/record.url?scp=84907899862&amp;partnerID=8YFLogxK</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">82</style></volume><pages><style face="normal" font="default" size="100%">223 - 227</style></pages><isbn><style face="normal" font="default" size="100%">0167-9473</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A class of partially reduced-bias estimators of a positive extreme value index (EVI), related to a mean-of-order-p class of EVI-estimators, is introduced and studied both asymptotically and for finite samples through a Monte-Carlo simulation study. A comparison between this class and a representative class of minimum-variance reduced-bias (MVRB) EVI-estimators is further considered. The MVRB EVI-estimators are related to a direct removal of the dominant component of the bias of a classical estimator of a positive EVI, the Hill estimator, attaining as well minimal asymptotic variance. Heuristic choices for the tuning parameters p and k, the number of top order statistics used in the estimation, are put forward, and applied to simulated and real data.A class of partially reduced-bias estimators of a positive extreme value index (EVI), related to a mean-of-order-p class of EVI-estimators, is introduced and studied both asymptotically and for finite samples through a Monte-Carlo simulation study. A comparison between this class and a representative class of minimum-variance reduced-bias (MVRB) EVI-estimators is further considered. The MVRB EVI-estimators are related to a direct removal of the dominant component of the bias of a classical estimator of a positive EVI, the Hill estimator, attaining as well minimal asymptotic variance. Heuristic choices for the tuning parameters p and k, the number of top order statistics used in the estimation, are put forward, and applied to simulated and real data.&lt;/p&gt;
</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;Sem PDF.National Funds through FCT-Fundacao para a Ciencia e a Tecnologia (PEst-OE/MAT/UI0006/2014; PEst-OE/MAT/UIO297/2014)&lt;/p&gt;
</style></notes><custom2><style face="normal" font="default" size="100%">10.1016/j.csda.2014.08.017</style></custom2></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gomes, M.I.</style></author><author><style face="normal" font="default" size="100%">Caeiro, F.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Eficiency of partially reduced-bias mean-of-order-p versus minimum-variance reduced-bias extreme value index estimation</style></title><secondary-title><style face="normal" font="default" size="100%">COMPSTAT 2014: 21th International Conference on Computational Statistics</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Proceedings of COMPSTAT 2014</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan</style></date></pub-dates></dates><urls><related-urls><url><style face="normal" font="default" size="100%">https://docentes.fct.unl.pt/sites/default/files/fac/files/gomes_caeiro_compstat2014_reprint.pdf</style></url></related-urls></urls><pub-location><style face="normal" font="default" size="100%">Geneve</style></pub-location><pages><style face="normal" font="default" size="100%">289-298</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;n/a&lt;/p&gt;
</style></abstract><work-type><style face="normal" font="default" size="100%">Peer reviewed</style></work-type><notes><style face="normal" font="default" size="100%">&lt;p&gt;n/a&lt;/p&gt;
</style></notes></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, F.</style></author><author><style face="normal" font="default" size="100%">Gomes, M. I:</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Pacheco, A.,</style></author><author><style face="normal" font="default" size="100%">Santos, R.,</style></author><author><style face="normal" font="default" size="100%">Rosário Oliveira, M.</style></author><author><style face="normal" font="default" size="100%">Paulino, C.D.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A semi-parametric estimator of a shape second order parameter.</style></title><secondary-title><style face="normal" font="default" size="100%">New Advances in Statistical Modeling and Applications</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Studies in Theoretical and Applied Statistics</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://link.springer.com/book/10.1007%2F978-3-319-05323-3</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">137-144</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;n/a&lt;/p&gt;
</style></abstract><work-type><style face="normal" font="default" size="100%">Peer reviewed</style></work-type><notes><style face="normal" font="default" size="100%">&lt;p&gt;n/a&lt;/p&gt;
</style></notes></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro F.</style></author><author><style face="normal" font="default" size="100%">Gomes M.I.</style></author><author><style face="normal" font="default" size="100%">Vandewalle B.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Semi-Parametric Probability-Weighted Moments Estimation Revisited</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;n/a&lt;/p&gt;
</style></abstract><work-type><style face="normal" font="default" size="100%">Peer reviewed</style></work-type><notes><style face="normal" font="default" size="100%">&lt;p&gt;n/a&lt;/p&gt;
</style></notes></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro,Frederico Almeida Gião Gonçalves</style></author><author><style face="normal" font="default" size="100%">Mateus,Ayana Maria Xavier Furtado</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An R implementation of several randomness tests</style></title><secondary-title><style face="normal" font="default" size="100%">AIP Conference Proceedings</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">nonparametric inference</style></keyword><keyword><style  face="normal" font="default" size="100%">R package</style></keyword><keyword><style  face="normal" font="default" size="100%">randomness test</style></keyword><keyword><style  face="normal" font="default" size="100%">rank</style></keyword><keyword><style  face="normal" font="default" size="100%">run</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2014/1/1</style></date></pub-dates></dates><pages><style face="normal" font="default" size="100%">531 - 534</style></pages><isbn><style face="normal" font="default" size="100%">978-0-7354-1255-2</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In many statistic methods, including distribution-free methods, we assume to work with random samples. In this note, we present randtests: an R package implementation of several nonparametric randomness tests. After a brief description of the tests included in the package, we present an application to real data sets in the field of Agricultural.In many statistic methods, including distribution-free methods, we assume to work with random samples. In this note, we present randtests: an R package implementation of several nonparametric randomness tests. After a brief description of the tests included in the package, we present an application to real data sets in the field of Agricultural.&lt;/p&gt;
</style></abstract><notes><style face="normal" font="default" size="100%">n/a</style></notes><custom2><style face="normal" font="default" size="100%">10.1063/1.4897792</style></custom2></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, Frederico</style></author><author><style face="normal" font="default" size="100%">Gomes, M. Ivette</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparison of Asymptotically Unbiased Extreme Value Index estimators: a Monte Carlo Simulation Study</style></title><secondary-title><style face="normal" font="default" size="100%">ICCMSE 2014</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><related-urls><url><style face="normal" font="default" size="100%">https://docentes.fct.unl.pt/sites/default/files/fac/files/caeiro_gomes_iccmse2014.pdf</style></url></related-urls></urls><pub-location><style face="normal" font="default" size="100%">Athens</style></pub-location><pages><style face="normal" font="default" size="100%">551-554</style></pages></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, F.</style></author><author><style face="normal" font="default" size="100%">Gomes, M.I.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On the bootstrap methodology for the estimation of the tail sample fraction</style></title><secondary-title><style face="normal" font="default" size="100%">COMPSTAT 2014: 21th International Conference on Computational Statistics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><related-urls><url><style face="normal" font="default" size="100%">https://docentes.fct.unl.pt/sites/default/files/fac/files/caeiro_gomes_compstat2014_reprint.pdf</style></url></related-urls></urls><pub-location><style face="normal" font="default" size="100%">Geneve</style></pub-location><pages><style face="normal" font="default" size="100%">546-553</style></pages></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, F.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Preface of the “Symposium on computational statistical methods”</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><related-urls><url><style face="normal" font="default" size="100%">https://docentes.fct.unl.pt/sites/default/files/fac/files/preface-web.pdf</style></url></related-urls></urls></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro F.</style></author><author><style face="normal" font="default" size="100%">Gomes</style></author><author><style face="normal" font="default" size="100%">M.I.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Asymptotic Comparison at Optimal Levels of Minimum-Variance Reduced-Bias Tail-Index Estimators</style></title><secondary-title><style face="normal" font="default" size="100%">Advances in Regression, Survival Analysis, Extreme Values, Markov Processes and Other Statistical Applications</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Studies in Theoretical and Applied Statistics</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Springer Berlin Heidelberg</style></publisher><pages><style face="normal" font="default" size="100%">83-91</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">n/a</style></abstract><work-type><style face="normal" font="default" size="100%">Peer reviewed</style></work-type><notes><style face="normal" font="default" size="100%">&lt;p&gt;Em minha opinião esta publicação devia ser considerada como {&quot;}proceeding{&quot;} e não como {&quot;}book-chapter{&quot;}: são comunicações de conferência.&lt;/p&gt;
</style></notes></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro F.</style></author><author><style face="normal" font="default" size="100%">Gomes M.I.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Class of Semi-parametric Probability Weighted Moment Estimators</style></title><secondary-title><style face="normal" font="default" size="100%">Recent Developments in Modeling and Applications in Statistics</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Studies in Theoretical and Applied Statistics</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://link.springer.com/chapter/10.1007/978-3-642-32419-2_15</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer Berlin Heidelberg</style></publisher><pages><style face="normal" font="default" size="100%">139-147</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">n/a</style></abstract><work-type><style face="normal" font="default" size="100%">Peer reviewed</style></work-type><notes><style face="normal" font="default" size="100%">&lt;p&gt;Springer: {&quot;}This volume contains a selection of contributions presented at the XVIII Annual Congress of the Portuguese Statistical Society.{&quot;} Deverá ser classificado como {&quot;}book chapter{&quot;} ou como {&quot;}proceeding{&quot;}?&lt;/p&gt;
</style></notes></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Furtado Ayana</style></author><author><style face="normal" font="default" size="100%">Caeiro Frederico</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparing several tests of randomness based on the difference of observations</style></title><tertiary-title><style face="normal" font="default" size="100%">AIP Conference Proceedings</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">1558</style></number><pages><style face="normal" font="default" size="100%">809-812</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;n/a&lt;/p&gt;
</style></abstract><work-type><style face="normal" font="default" size="100%">Peer reviewed</style></work-type><notes><style face="normal" font="default" size="100%">&lt;p&gt;WOS: aguarda confirmação.&lt;/p&gt;
</style></notes></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro F.</style></author><author><style face="normal" font="default" size="100%">Gomes M.I.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On the Selection of the Tuning Parameter of a Moment Estimator of the Extreme Value Index</style></title><tertiary-title><style face="normal" font="default" size="100%">AIP conference proceedings</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">1558</style></number><pages><style face="normal" font="default" size="100%">801-804</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;n/a&lt;/p&gt;
</style></abstract><work-type><style face="normal" font="default" size="100%">Peer reviewed</style></work-type><notes><style face="normal" font="default" size="100%">&lt;p&gt;WOS: aguarda confirmação Scopus: não foi possível verificar&lt;/p&gt;
</style></notes></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gomes M.I.</style></author><author><style face="normal" font="default" size="100%">Henriques-Rodrigues L.</style></author><author><style face="normal" font="default" size="100%">Caeiro F.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Torelli, Nicola; Pesarin, Fortunato; Bar-Hen, Avner (Eds.)</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Refined Estimation of a Light Tail: An Application to Environmental Data</style></title><secondary-title><style face="normal" font="default" size="100%">Advances in Theoretical and Applied Statistics</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Studies in Theoretical and Applied Statistics</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://link.springer.com/chapter/10.1007/978-3-642-35588-2_14</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer Berlin Heidelberg</style></publisher><pages><style face="normal" font="default" size="100%">143-153</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">n/a</style></abstract><work-type><style face="normal" font="default" size="100%">Peer reviewed</style></work-type><notes><style face="normal" font="default" size="100%">&lt;p&gt;Springer: {&quot;}This volume includes contributions selected after a double blind review process and presented as a preliminary version at the 45th Meeting of the Italian Statistical Society.{&quot;} Deverá ser classificado como {&quot;}book chapter{&quot;} ou como {&quot;}proceeding{&quot;}?&lt;/p&gt;
</style></notes></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro F.</style></author><author><style face="normal" font="default" size="100%">Gomes, M.I.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Role of Bootstrap Methodologies in the Estimation of a Negative Extreme Value Index.</style></title><tertiary-title><style face="normal" font="default" size="100%">Proceedings 59th ISI World Statistics Congress</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan</style></date></pub-dates></dates><pages><style face="normal" font="default" size="100%">103-108</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;n/a&lt;/p&gt;
</style></abstract><work-type><style face="normal" font="default" size="100%">Peer reviewed</style></work-type><notes><style face="normal" font="default" size="100%">&lt;p&gt;n/a&lt;/p&gt;
</style></notes></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lita da Silva, J.</style></author><author><style face="normal" font="default" size="100%">Caeiro, F.</style></author><author><style face="normal" font="default" size="100%">Natário, I.</style></author><author><style face="normal" font="default" size="100%">Braumann, C.A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Advances in Regression, Survival Analysis, Extreme Values, Markov Processes and Other Statistical Applications</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><related-urls><url><style face="normal" font="default" size="100%">https://docentes.fct.unl.pt/sites/default/files/fac/files/productflyer_978-3-642-34903-4.pdf</style></url></related-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Berlin Heidelberg</style></pub-location></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>36</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, F.</style></author><author><style face="normal" font="default" size="100%">Gomes, M.I.</style></author><author><style face="normal" font="default" size="100%">Henriques-Rodrigues, L.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A location invariant probability weighted moment EVI-estimator</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><related-urls><url><style face="normal" font="default" size="100%">https://docentes.fct.unl.pt/sites/default/files/fac/files/2013_30_port-ppwm-final.pdf</style></url></related-urls></urls><publisher><style face="normal" font="default" size="100%">Notas e Comunicações do CEAUL 30/2013</style></publisher></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Beirlant J</style></author><author><style face="normal" font="default" size="100%">Caeiro F</style></author><author><style face="normal" font="default" size="100%">Gomes MI</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An Overview And Open Research Topics In Statistics Of Univariate Extremes</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">10</style></volume><pages><style face="normal" font="default" size="100%">1-31</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">n/a</style></abstract><work-type><style face="normal" font="default" size="100%">Peer reviewed</style></work-type><notes><style face="normal" font="default" size="100%">n/a</style></notes></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gomes M.I.</style></author><author><style face="normal" font="default" size="100%">Caeiro F.</style></author><author><style face="normal" font="default" size="100%">Henriques-Rodrigues L.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">PORT-PPWM extreme value index estimation</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of COMPSTAT 2012</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Proceedings of COMPSTAT 2012</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan</style></date></pub-dates></dates><urls><related-urls><url><style face="normal" font="default" size="100%">https://docentes.fct.unl.pt/sites/default/files/fac/files/2012_compstat2012.pdf</style></url></related-urls></urls><pages><style face="normal" font="default" size="100%">259-270</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;n/a&lt;/p&gt;
</style></abstract><work-type><style face="normal" font="default" size="100%">Peer reviewed</style></work-type><notes><style face="normal" font="default" size="100%">&lt;p&gt;n/a&lt;/p&gt;
</style></notes></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro F.</style></author><author><style face="normal" font="default" size="100%">Gomes M.I.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Reduced Bias Estimator of a 'Scale' Second Order Parameter</style></title><tertiary-title><style face="normal" font="default" size="100%">AIP Conference Proceedings</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Jan</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">1479</style></number><pages><style face="normal" font="default" size="100%">1114-1117</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;n/a&lt;/p&gt;
</style></abstract><work-type><style face="normal" font="default" size="100%">Peer reviewed</style></work-type><notes><style face="normal" font="default" size="100%">&lt;p&gt;n/a&lt;/p&gt;
</style></notes></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, F.</style></author><author><style face="normal" font="default" size="100%">Gomes, M.I.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Probability weighted moments bootstrap estimation: a case study in the field of insurance</style></title><secondary-title><style face="normal" font="default" size="100%">Risk and Extreme Values in Insurance and Finance: Book of Abstracts</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><related-urls><url><style face="normal" font="default" size="100%">https://docentes.fct.unl.pt/sites/default/files/fac/files/rev2011_caeiro_gomes.pdf</style></url></related-urls></urls><publisher><style face="normal" font="default" size="100%">CEAUL</style></publisher><pub-location><style face="normal" font="default" size="100%">Lisbon</style></pub-location><pages><style face="normal" font="default" size="100%">27-30</style></pages></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, Frederico</style></author><author><style face="normal" font="default" size="100%">Gomes, M.Ivette</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Semi-parametric tail inference through probability-weighted moments.</style></title><secondary-title><style face="normal" font="default" size="100%">J. Stat. Plann. Inference</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">extreme value index</style></keyword><keyword><style  face="normal" font="default" size="100%">first order scale parameter</style></keyword><keyword><style  face="normal" font="default" size="100%">semi-parametric estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">statistics of extremes}</style></keyword><keyword><style  face="normal" font="default" size="100%">{heavy tails</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">141</style></volume><pages><style face="normal" font="default" size="100%">937-950</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;{Summary: For heavy-tailed models, and working with the sample of the $k$ largest observations, we present probability weighted moments (PWM) estimators for the first order tail parameters. Under regular variation conditions on the right-tail of the underlying distribution function $F$ we prove the consistency and asymptotic normality of these estimators. Their performance, for finite sample sizes, is illustrated through a small-scale Monte Carlo simulation.}&lt;/p&gt;
</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;n/a&lt;/p&gt;
</style></notes></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, Frederico</style></author><author><style face="normal" font="default" size="100%">Gomes, M.Ivette</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An asymptotically unbiased moment estimator of a negative extreme value index.</style></title><secondary-title><style face="normal" font="default" size="100%">Discuss. Math., Probab. Stat.</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">{semi-parametric estimation}</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">30</style></volume><pages><style face="normal" font="default" size="100%">5-19</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;{Summary: We consider a new class of consistent semi-parametric estimators of a negative extreme value index, based on the set of the $k$ largest observations. This class of estimators depends on a control or tuning parameter, which enables us to have access to an estimator with a null second-order component of asymptotic bias, and with a rather interesting mean squared error, as a function of $k$. We study the consistency and asymptotic normality of the proposed estimators. Their finite sample behaviour is obtained through Monte Carlo simulation.}&lt;/p&gt;
</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;n/a&lt;/p&gt;
</style></notes></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, F.</style></author><author><style face="normal" font="default" size="100%">Gomes, M.I.</style></author><author><style face="normal" font="default" size="100%">Pestana, D.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Oliveira, I., Correia, E., Ferreira, F. Dias, S. e Braumann, C.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Alguns resultados adicionais sobre a variância de um estimador de viés reduzido do índice de cauda.</style></title><secondary-title><style face="normal" font="default" size="100%">Actas do XVI Congresso Anual da Sociedade Portuguesa de Estatística - &quot;Arte de Explicar o Acaso&quot;.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><related-urls><url><style face="normal" font="default" size="100%">https://docentes.fct.unl.pt/sites/default/files/fac/files/2009spe_art016.pdf</style></url></related-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;n/a&lt;/p&gt;
</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;n/a&lt;/p&gt;
</style></notes></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gomes, M.Ivette</style></author><author><style face="normal" font="default" size="100%">Pestana, Dinis</style></author><author><style face="normal" font="default" size="100%">Caeiro, Frederico</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A note on the asymptotic variance at optimal levels of a bias-corrected Hill estimator.</style></title><secondary-title><style face="normal" font="default" size="100%">Stat. Probab. Lett.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">79</style></volume><pages><style face="normal" font="default" size="100%">295-303</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;n/a&lt;/p&gt;
</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;n/a&lt;/p&gt;
</style></notes></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, Frederico</style></author><author><style face="normal" font="default" size="100%">Gomes, M.Ivette</style></author><author><style face="normal" font="default" size="100%">Rodrigues, Lígia Henriques</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Reduced-bias tail index estimators under a third-order framework.</style></title><secondary-title><style face="normal" font="default" size="100%">Commun. Stat., Theory Methods</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">heavy tails</style></keyword><keyword><style  face="normal" font="default" size="100%">semi-parametric estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">statistics of extremes}</style></keyword><keyword><style  face="normal" font="default" size="100%">{bias estimation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><number><style face="normal" font="default" size="100%">7</style></number><volume><style face="normal" font="default" size="100%">38</style></volume><pages><style face="normal" font="default" size="100%">1019-1040</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;{Summary: We are interested in the comparison, under a third-order framework, of classes of second-order, reduced-bias tail index estimators, giving particular emphasis to minimum-variance reduced-bias estimators of the tail index $\gamma$. The full asymptotic distributional properties of the proposed classes are derived under a third-order framework and the estimators are compared with other alternatives, not only asymptotically, but also for finite samples through Monte Carlo techniques. An application to the log-exchange rates of the Euro against the USA Dollar is also provided.}&lt;/p&gt;
</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;n/a&lt;/p&gt;
</style></notes></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, Frederico</style></author><author><style face="normal" font="default" size="100%">Gomes, M.Ivette</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Semi-parametric second-order reduced-bias high quantile estimation.</style></title><secondary-title><style face="normal" font="default" size="100%">Test</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">high quantiles</style></keyword><keyword><style  face="normal" font="default" size="100%">semi-parametric estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">statistics of extremes}</style></keyword><keyword><style  face="normal" font="default" size="100%">{heavy tails</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">392-413</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;{Summary: In many areas of application, like, for instance, climatology, hydrology, insurance, finance, and statistical quality control, a typical requirement is to estimate a high quantile of probability $1 - p$, a value high enough so that the chance of an exceedance of that value is equal to $p$, small. The semi-parametric estimation of high quantiles depends not only on the estimation of the tail index or extreme value index $\gamma $, the primary parameter of extreme events, but also on the adequate estimation of a scale first order parameter. Recently, apart from new classes of reduced-bias estimators for $\gamma &amp;gt;0$, new classes of the scale first order parameter have been introduced in the literature. Their use in quantile estimation enables us to introduce new classes of asymptotically unbiased high quantiles' estimators, with the same asymptotic variance as the (biased) ``classical'' estimator. The asymptotic distributional properties of the proposed classes of estimators are derived and the estimators are compared with alternative ones, not only asymptotically, but also for finite samples through Monte Carlo techniques. An application to the log-exchange rates of the Euro against the Sterling Pound is also provided.}&lt;/p&gt;
</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;n/a&lt;/p&gt;
</style></notes></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, F.</style></author><author><style face="normal" font="default" size="100%">Gomes, M.I.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Caudas pesadas: t de Student e variante assimétrica versus metodologia semi-paramétrica.</style></title><secondary-title><style face="normal" font="default" size="100%">Actas do XV Congresso Anual da Sociedade Portuguesa de Estatística - “Da Teoria à Prática”</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><related-urls><url><style face="normal" font="default" size="100%">https://docentes.fct.unl.pt/sites/default/files/fac/files/art053.pdf</style></url></related-urls></urls><pub-location><style face="normal" font="default" size="100%">Lisboa</style></pub-location><pages><style face="normal" font="default" size="100%">127-136</style></pages></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, Frederico</style></author><author><style face="normal" font="default" size="100%">Gomes, M.Ivette</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Minimum-variance reduced-bias tail index and high quantile estimation.</style></title><secondary-title><style face="normal" font="default" size="100%">REVSTAT</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">second-order reduced-bias semiparametric estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">third order framework}</style></keyword><keyword><style  face="normal" font="default" size="100%">{statistics of extremes</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">1-20</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;{Summary: Heavy tailed-models are quite useful in many fields, like insurance, finance, telecommunications, internet traffic, among others, and it is often necessary to estimate a high quantile, i.e., a value that is exceeded with a probability $p$, small. The semiparametric estimation of this parameter relies essentially on the estimation of the tail index, the primary parameter in statistics of extremes. Classical semi-parametric estimators of extreme parameters show usually a severe bias and are known to be very sensitive to the number $k$ of top order statistics used in the estimation. For $k$ small they have a high variance, and for large $k$ a high bias. Recently, new second-order ``shape'' and ``scale'' estimators allowed the development of second-order reduced-bias estimators, which are much less sensitive to the choice of $k$. Here we study, under a third order framework, minimum-variance reduced-bias (MVRB) tail index estimators, recently introduced in the literature, and dependent on an adequate estimation of second order parameters. The improvement comes from the asymptotic variance, which is kept equal to the asymptotic variance of the classical Hill estimator [ıt B. Hill}, Ann. Stat. 3, 1163–1174 (1975; Zbl 0323.62033)] provided that we estimate the second order parameters at a level of a larger order than the level used for the estimation of the first order parameter. The use of those MVRB tail index estimators enables us to introduce new classes of reduced-bias high quantile estimators. These new classes are compared among themselves and with previous ones through the use of a small-scale Monte Carlo simulation.}&lt;/p&gt;
</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;n/a&lt;/p&gt;
</style></notes></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, F.</style></author><author><style face="normal" font="default" size="100%">Gomes, M.I.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Second-order Reduced-bias Tail Index and High Quantile Estimation</style></title><secondary-title><style face="normal" font="default" size="100%">56th SESSION OF THE INTERNATIONAL STATISTICAL INSTITUTE</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><urls><related-urls><url><style face="normal" font="default" size="100%">https://docentes.fct.unl.pt/sites/default/files/fac/files/2007isi_volume_lxii_proceedings_caeiro_gomes.pdf</style></url><url><style face="normal" font="default" size="100%">https://docentes.fct.unl.pt/sites/default/files/fac/files/2007isi_volume_lxii_proceedings_caeiro_gomes_0.pdf</style></url></related-urls></urls><pub-location><style face="normal" font="default" size="100%">Lisbon, Portugal</style></pub-location><pages><style face="normal" font="default" size="100%">109-116</style></pages></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, F.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Estimação de Parâmetros de Acontecimentos Raros</style></title><secondary-title><style face="normal" font="default" size="100%">FCUL</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><urls><related-urls><url><style face="normal" font="default" size="100%">https://docentes.fct.unl.pt/sites/default/files/fac/files/2006caeiro-eapr.pdf</style></url></related-urls></urls><pub-location><style face="normal" font="default" size="100%">Lisbon</style></pub-location><work-type><style face="normal" font="default" size="100%">PhD thesis</style></work-type></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, F.</style></author><author><style face="normal" font="default" size="100%">Gomes, M.I.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Estimação de quantis elevados em estatística de extremos</style></title><secondary-title><style face="normal" font="default" size="100%">Actas do XIII Congresso Anual da Sociedade Portuguesa de Estatística - &quot;Ciência Estatística&quot;</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><urls><related-urls><url><style face="normal" font="default" size="100%">https://docentes.fct.unl.pt/sites/default/files/fac/files/2006spe217-228.pdf</style></url></related-urls></urls><pages><style face="normal" font="default" size="100%">217-228</style></pages></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, Frederico</style></author><author><style face="normal" font="default" size="100%">Gomes, M.Ivette</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A new class of estimators of a ``scale'' second order parameter.</style></title><secondary-title><style face="normal" font="default" size="100%">Extremes</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">asymptotic normality}</style></keyword><keyword><style  face="normal" font="default" size="100%">heavy tails</style></keyword><keyword><style  face="normal" font="default" size="100%">tail moments</style></keyword><keyword><style  face="normal" font="default" size="100%">{tail index</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><number><style face="normal" font="default" size="100%">3-4</style></number><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">193-211</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;{Let $X_i$ be i.i.d. r.v.s with heavy-tailed CDF $F(x)$ such that $$1-F(x)=(x/C)^{-1/\gamma}((1+(\beta/\rho)(x/C)^{\rho/\gamma} +\beta'(x/C)^{2\rho/\gamma}(1+o(1))),$$ where $\gamma$ is the tail index ($\gamma&amp;gt;0$), and $\rho&amp;lt;0$ and $\beta$ are the ``second order parameters''. The authors construct an estimator for $\beta$ based on the ``tail moments'' $$M_n^{(\alpha)}=(k)^{-1}\sum_{i=1}^k [łog X_{n-i+1:n}-łog X_{n-k:n}]^\alpha. $$ Consistency and asymptotic normality of the estimator are demonstrated. The small sample properties of the estimator are investigated via simulations.}&lt;/p&gt;
</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;n/a&lt;/p&gt;
</style></notes></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, F.</style></author><author><style face="normal" font="default" size="100%">Gomes, M.I.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Redução de viés na estimação semi-paramétrica de um parâmetro de escala</style></title><secondary-title><style face="normal" font="default" size="100%">Actas do XIII Congresso da SPE - &quot;Ciência Estatística&quot;</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><urls><related-urls><url><style face="normal" font="default" size="100%">https://docentes.fct.unl.pt/sites/default/files/fac/files/2006spe_127-148.pdf</style></url></related-urls></urls><pages><style face="normal" font="default" size="100%">127-148</style></pages></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, Frederico</style></author><author><style face="normal" font="default" size="100%">Gomes, M.Ivette</style></author><author><style face="normal" font="default" size="100%">Pestana, Dinis</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Direct reduction of bias of the classical Hill estimator.</style></title><secondary-title><style face="normal" font="default" size="100%">REVSTAT</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">bias estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">heavy tails}</style></keyword><keyword><style  face="normal" font="default" size="100%">semi-parametric estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">{statistics of extremes</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">3</style></volume><pages><style face="normal" font="default" size="100%">113-136</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;{Summary: We are interested in an adequate estimation of the dominant component of the bias of ıt B. M. Hill}\,'s estimator [Ann. Stat. 3, 1163–1174 (1975; Zbl 0323.62033)] of a positive tail index $\gamma$, in order to remove it from the classical Hill estimator in different asymptotically equivalent ways. If the second order parameters in the bias are computed at an adequate level $k_1$ of a larger order than that of the level $k$ at which the Hill estimator is computed, there may be no change in the asymptotic variances of these reduced bias tail index estimators, which are kept equal to the asymptotic variance of the Hill estimator, i.e., equal to $\gamma^2$. The asymptotic distributional properties of the proposed estimators of $\gamma$ are derived and the estimators are compared not only asymptotically, but also for finite samples through Monte Carlo techniques.}&lt;/p&gt;
</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;n/a&lt;/p&gt;
</style></notes></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, F.</style></author><author><style face="normal" font="default" size="100%">Gomes, M.I.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Uma classe de estimadores do parâmetro de escala de segunda ordem.</style></title><secondary-title><style face="normal" font="default" size="100%">Actas do XII Congresso Anual da Sociedade Portuguesa de Estatística</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><urls><related-urls><url><style face="normal" font="default" size="100%">https://docentes.fct.unl.pt/sites/default/files/fac/files/caeirof-spe2004.pdf</style></url></related-urls></urls><pub-location><style face="normal" font="default" size="100%">Évora, Portugal</style></pub-location><pages><style face="normal" font="default" size="100%">113-124</style></pages></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gomes, M.Ivette</style></author><author><style face="normal" font="default" size="100%">Caeiro, Frederico</style></author><author><style face="normal" font="default" size="100%">Figueiredo, Fernanda</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bias reduction of a tail index estimator through an external estimation of the second-order parameter.</style></title><secondary-title><style face="normal" font="default" size="100%">Statistics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">semi-parametric estimation}</style></keyword><keyword><style  face="normal" font="default" size="100%">{theory of extremes</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><number><style face="normal" font="default" size="100%">6</style></number><volume><style face="normal" font="default" size="100%">38</style></volume><pages><style face="normal" font="default" size="100%">497-510</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;{Summary: We first consider a class of consistent semi-parametric estimators of a positive tail index $\gamma$, parametrised in a tuning or control parameter $\alpha$. Such a control parameter enables us to have access for any available sample, to an estimator of the tail index $\gamma$ with a null dominant component of asymptotic bias and consequently with a reasonably flat mean squared error pattern, as a function of $k$, the number of top-order statistics considered.\par Such a control parameter depends on a second-order parameter $\rho$, which will be adequately estimated so that we may achieve a high efficiency relative to the classical Hill estimator [ıt B. M. Hill}, Ann. Stat. 3, 1163–1174 (1975; Zbl 0323.62033)] provided we use a number of top-order statistics larger than the one usually required for the estimation through the Hill estimator. An illustration of the behaviour of the estimators is provided, through the analysis of the daily log-returns on the Euro-US\$ exchange rates.}&lt;/p&gt;
</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;n/a&lt;/p&gt;
</style></notes></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, F.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Redução de viés em estimadores do índice de cauda</style></title><secondary-title><style face="normal" font="default" size="100%">Actas do X Congresso Anual da SPE - “Literacia e Estatística”</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><urls><related-urls><url><style face="normal" font="default" size="100%">https://docentes.fct.unl.pt/sites/default/files/fac/files/spe2002_187-199.pdf</style></url></related-urls></urls><pub-location><style face="normal" font="default" size="100%">Porto, Portugal</style></pub-location><pages><style face="normal" font="default" size="100%">187-199</style></pages></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, Frederico</style></author><author><style face="normal" font="default" size="100%">Gomes, M. Ivette</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bias reduction in the estimation of parameters of rare events.</style></title><secondary-title><style face="normal" font="default" size="100%">Theory of Stochastic Processes</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">estimators</style></keyword><keyword><style  face="normal" font="default" size="100%">heavy tails}</style></keyword><keyword><style  face="normal" font="default" size="100%">rare events</style></keyword><keyword><style  face="normal" font="default" size="100%">{bias reduction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><urls><related-urls><url><style face="normal" font="default" size="100%">https://docentes.fct.unl.pt/sites/default/files/fac/files/2002tsp_caeiro_gomes.pdf</style></url></related-urls></urls><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">67-76</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;{Consider the distribution function $EV_{\gamma}(x)=\exp(-(1+\gamma x)^{- 1/\gamma}),\ \gamma&amp;gt;0,\ 1+\gamma x&amp;gt;0$, to which $\max(X_{1},łdots, X_{n})$ is attracted after suitable linear normalization. The authors consider the underlying model $F$ in the max-domain of attraction of $EV_{\gamma}$, where $ X_{i:n},\ 1łeq Iłeq n$, denotes the i-th ascending order statistic associated to the random sample $(X_{1},łdots, X_{n})$ from the unknown distribution function $F$. This article is devoted to studying semi-parametric estimators of $\gamma$ in the form $$\gamma_{n}^{(þeta,\alpha)}(k)=(\Gamma(\alpha)/M_{n}^{(\alpha- 1)}(k))łeft(M_{n}^{(þeta\alpha)}(k)/\Gamma(þeta\alpha+1)\right) ^{1/þeta},\quad \alpha\geq 1,\quad þeta&amp;gt;0,$$ parametrized by the parameters $\alpha$ and $þeta$, which may be controlled, where $M_{n}^{(0)}=1$ and $ M_{n}^{(\alpha)}(k)=k^{-1}\sum_{i=1}^{k}(łn X_{n-i+1:n}-łn X_{n-k:n})^{\alpha}$, $\alpha&amp;gt;0$, is a consistent estimator of $\Gamma(\alpha+1)\gamma^{\alpha}$, as $k\toınfty$, and $k=o(n)$, as $n\toınfty$.\par The authors derive the asymptotic distributional properties of the considered class of estimators and obtain that for $þeta&amp;gt;1$ it is always possible to find a control parameter $\alpha$ which makes the dominant component of the asymptotic bias of the proposed estimator null and depends on the second order parameter $\rho$. An investigation of the $\rho$-estimator is presented.}&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">24</style></issue><notes><style face="normal" font="default" size="100%">&lt;p&gt;n/a&lt;/p&gt;
</style></notes></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Caeiro, Frederico</style></author><author><style face="normal" font="default" size="100%">Gomes, M. Ivette</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A class of asymptotically unbiased semi-parametric estimators of the tail index.</style></title><secondary-title><style face="normal" font="default" size="100%">Test</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">finite sample behavior</style></keyword><keyword><style  face="normal" font="default" size="100%">robustness</style></keyword><keyword><style  face="normal" font="default" size="100%">simulations}</style></keyword><keyword><style  face="normal" font="default" size="100%">Student models</style></keyword><keyword><style  face="normal" font="default" size="100%">{extremes</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">345-364</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;{Summary: We consider a class of consistent semi-parametric estimators of a positive tail index $\gamma$, parameterized by a tuning or control parameter $\alpha$. Such a control parameter enables us to have access, for any available sample, to an estimator of $\gamma$ with a null dominant component of asymptotic bias, and with a reasonably flat mean squared error pattern, as a function of $k$, the number of top order statistics considered. Moreover, we are able to achieve a high efficiency relative to the classical Hill estimator [ıt B. M. Hill}, Ann. Stat. 3, 1163–1174 (1975; Zbl 0323.62033)], provided we may have access to a larger number of top order statistics than the number needed for optimal estimation through the Hill estimator.}&lt;/p&gt;
</style></abstract><notes><style face="normal" font="default" size="100%">&lt;p&gt;n/a&lt;/p&gt;
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