Publications

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2022
Caeiro, F., Henriques-Rodrigues L. {\'ı}gia, & Gomes I. M. (2022).  The Use of Generalized Means in the Estimation of the Weibull Tail Coefficient. (Anil Kumar, Ed.).Computational and Mathematical Methods. 2022, 1–12., jun: Hindawi Limited AbstractWebsite
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2020
Caeiro, F., Henriques-Rodrigues L. {\'ı}gia, Gomes I. M., & Cabral I. (2020).  Minimum-variance reduced-bias estimation of the extreme value index: A theoretical and empirical study. Computational and Mathematical Methods. , may: Wiley AbstractWebsite
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Gomes, M. I., Caeiro F., Figueiredo F., Henriques-Rodrigues L., & Pestana D. (2020).  Corrected-Hill versus partially reduced-bias value-at-risk estimation. Communications in Statistics: Simulation and Computation. 49, 867-885., Number 4 Abstract
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Gomes, M. I., Caeiro F., Figueiredo F., Henriques-Rodrigues L., & Pestana D. (2020).  Reduced-bias and partially reduced-bias mean-of-order-p value-at-risk estimation: a Monte-Carlo comparison and an application. Journal of Statistical Computation and Simulation. 90, 1735-1752., Number 10 Abstract
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2019
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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Caeiro, F., & Henriques-Rodrigues L. (2019).  Reduced-bias kernel estimators of a positive extreme value index. Mathematical Methods in the Applied Sciences. 42, 5867-5880., Number 17 Abstract
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2016
Caeiro, F. A. G. G., Gomes I. M., & Henriques-Rodrigues L. (2016).  A location-invariant probability weighted moment estimation of the Extreme Value Index. International Journal of Computer Mathematics. 93(4), 676 - 695., 2016/4/2 AbstractWebsite

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.

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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2013
Caeiro, F., Gomes M. I., & Henriques-Rodrigues L. (2013).  A location invariant probability weighted moment EVI-estimator. : Notas e Comunicações do CEAUL 30/20132013_30_port-ppwm-final.pdf