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Caeiro, F., & Gomes M. I. (2007).  Second-order Reduced-bias Tail Index and High Quantile Estimation. 56th SESSION OF THE INTERNATIONAL STATISTICAL INSTITUTE. 109-116., Lisbon, Portugal2007isi_volume_lxii_proceedings_caeiro_gomes.pdf
Caeiro, F., & Gomes M. I.: (2014).  A semi-parametric estimator of a shape second order parameter.. (Pacheco, A.,, Santos, R.,, Rosário Oliveira, M., Paulino, C.D., Ed.).New Advances in Statistical Modeling and Applications. 137-144., Jan: Springer 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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Caeiro, F., & Gomes M. I. (2009).  Semi-parametric second-order reduced-bias high quantile estimation.. Test. 18, 392-413., Number 2 Abstract

{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 $\gamma >0$, 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.}

Caeiro, F., & Gomes M. I. (2011).  Semi-parametric tail inference through probability-weighted moments.. J. Stat. Plann. Inference. 141, 937-950., Number 2 Abstract

{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.}

Caeiro, F., Henriques-Rodrigues L. {\'ı}gia, & Gomes D. P. (2019).  A simple class of reduced bias kernel estimators of extreme value parameters. Computational and Mathematical Methods. e1025., apr: Wiley AbstractWebsite
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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.