Publications

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F., C., Gomes, & M.I. (2013).  Asymptotic Comparison at Optimal Levels of Minimum-Variance Reduced-Bias Tail-Index Estimators. Advances in Regression, Survival Analysis, Extreme Values, Markov Processes and Other Statistical Applications. 83-91., Jan: Springer Berlin Heidelberg Abstract
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Gomes, I. M., Caeiro F., Henriques-Rodrigues L., & Manjunath B. g (2016).  Bootstrap Methods in Statistics of Extremes. Extreme Events in Finance. 117 - 138., 2016/10/7: John Wiley & Sons, Inc. Abstract
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F., C., & M.I. G. (2013).  A Class of Semi-parametric Probability Weighted Moment Estimators. Recent Developments in Modeling and Applications in Statistics. 139-147., Jan: Springer Berlin Heidelberg Abstract
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Caeiro, F., Mateus A., & Soltane L. (2021).  A class of weighted Hill estimators. Computational and Mathematical Methods. , may: Wiley AbstractWebsite
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Mateus, A., & Caeiro F. (2022).  Confidence Intervals for the Shape Parameter of a Pareto Distribution. AIP Conference Proceedings. 2425, Abstract
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Penalva, H., Ivette Gomes M., Caeiro F., & Manuela Neves M. (2020).  A couple of non reduced bias generalized means in extreme value theory: An asymptotic comparison. Revstat Statistical Journal. 18, 281-298., Number 3 Abstract
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Mateus, A. M. X. F., & Caeiro F. A. G. G. (2015).  The difference-sign randomness test. NTERNATIONAL CONFERENCE OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2015. 1702, , 2015: American Institute of Physics Inc. Abstract

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.

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Caeiro, F., & Mateus A. (2018).  Empirical Power Study of the Jackson Exponentiality Test. Demography and Health Issues. 225–235.: Springer International Publishing Abstract
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Mateus, A., & Caeiro F. (2018).  Exact and Approximate Probabilities for the Null Distribution of Bartels Randomness Test. Contributions to Statistics. 227–240.: Springer International Publishing Abstract
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Caeiro, F., & Mateus A. (2022).  Exponential versus Generalized Exponential Distribution: a Computational Study. AIP Conference Proceedings. 2425, Abstract
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Caeiro, F. A. G. G., Mateus A. M. X. F., & Ramos L. P. C. (2015).  Extreme value analysis of the sea levels in Venice. PROCEEDINGS OF THE INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2014. , 2015: American Institute of Physics Inc. Abstract

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.

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Caeiro, F., Martins A. P., & Sequeira I. J. (2015).  Finite sample behaviour of classical and quantile regression estimators for the Pareto distribution. Proceedings of the International Conference on Numerical Analysis and Applied Mathematics 2014, ICNAAM 2014. 1648, , 2015/3/10: American Institute of Physics Inc. Abstract

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.

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Mateus, A., & Caeiro F. (2022).  Improved Shape Parameter Estimation for the Three-Parameter Log-Logistic Distribution. (Qichun Zhang, Ed.).Computational and Mathematical Methods. 2022, 1–13., feb: Hindawi Limited AbstractWebsite
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Mateus, A., & Caeiro F. (2020).  A new class of estimators for the shape parameter of a Pareto model. Computational and Mathematical Methods. , nov: Wiley AbstractWebsite
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Caeiro, F., & Mateus A. (2023).  A New Class of Generalized Probability-Weighted Moment Estimators for the Pareto Distribution. Mathematics. 11, 1076., feb, Number 5: {MDPI} {AG} AbstractWebsite
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Caeiro, F., Marques F. J., Mateus A., & Atal S. (2016).  A note on the Jackson exponentiality test. International Conference of Computational Methods in Sciences and Engineering 2016, ICCMSE 2016. 1790, , 2016/12/6: American Institute of Physics Inc. Abstract

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.

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F., C., & M.I. G. (2013).  On the Selection of the Tuning Parameter of a Moment Estimator of the Extreme Value Index. 801-804., Jan, Number 1558 Abstract

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J, B., F C., & MI G. (2012).  An Overview And Open Research Topics In Statistics Of Univariate Extremes. 10, 1-31., Jan Abstract
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M.I., G., F. C., & L. H. - R. (2012).  PORT-PPWM extreme value index estimation. Proceedings of COMPSTAT 2012. 259-270., Jan Abstract2012_compstat2012.pdf

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Caeiro, F. A. G. G., & Mateus A. M. X. F. (2014).  An R implementation of several randomness tests. AIP Conference Proceedings. 531 - 534., 2014/1/1 Abstract

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.

F., C., & M.I. G. (2012).  A Reduced Bias Estimator of a 'Scale' Second Order Parameter. 1114-1117., Jan, Number 1479 Abstract

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M.I., G., L. H. - R., & F. C. (2013).  Refined Estimation of a Light Tail: An Application to Environmental Data. (Torelli, Nicola; Pesarin, Fortunato; Bar-Hen, Avner (Eds.), Ed.).Advances in Theoretical and Applied Statistics. 143-153., Jan: Springer Berlin Heidelberg Abstract
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F., C., M.I. G., & B. V. (2014).  Semi-Parametric Probability-Weighted Moments Estimation Revisited. , Jan Abstract

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Mateus, A., Caeiro F., Gomes D. P., & Sequeira I. J. (2016).  Statistical analysis of extreme river flows. International Conference of Computational Methods in Sciences and Engineering 2016, ICCMSE 2016. 1790, , 2016/12/6: American Institute of Physics Inc. Abstract

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.