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Computational Study of the Adaptive Estimation of the Extreme Value Index with Probability Weighted Moments

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Recent Developments in Statistics and Data Science (SPE 2021)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 398))

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Abstract

In statistics of extremes, the estimation of the extreme value index (EVI) is an important and central topic of research. We consider the probability weighted moment estimator of the EVI, based on the largest observations. Due to the specificity of the properties of the estimator, a direct estimation of the threshold is not straightforward. In this work, we consider an adaptive choice of the number of order statistics based on the double bootstrap methodology. Computational and empirical properties of the methodology are here provided.

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Acknowledgements

Research partially supported by National Funds through FCT—Fundação para a Ciência e a Tecnologia, projects UIDB/00006/2020 and UIDP/00006/2020 (CEA/UL) and UIDB/00297/2020 and UIDP/00297/2020 (CMA/UNL).

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Authors and Affiliations

  1. NOVA School of Science and Technology (FCT NOVA) and CMA, NOVA University of Lisbon, Campus de Caparica, Lisbon, Portugal

    Frederico Caeiro

  2. Faculty of Science of Lisbon (FCUL/DEIO) and CEAUL, University of Lisbon, Cidade Universitária, Campo Grande, Portugal

    M. Ivette Gomes

Authors
  1. Frederico Caeiro
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  2. M. Ivette Gomes
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Corresponding author

Correspondence to Frederico Caeiro .

Editor information

Editors and Affiliations

  1. NOVA School of Science and Technology, Caparica, Portugal

    Regina Bispo

  2. University of Évora, Évora, Portugal

    Lígia Henriques-Rodrigues

  3. University of Évora, Évora, Portugal

    Russell Alpizar-Jara

  4. University of Edinburgh, Edinburgh, UK

    Miguel de Carvalho

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Caeiro, F., Gomes, M.I. (2022). Computational Study of the Adaptive Estimation of the Extreme Value Index with Probability Weighted Moments. In: Bispo, R., Henriques-Rodrigues, L., Alpizar-Jara, R., de Carvalho, M. (eds) Recent Developments in Statistics and Data Science. SPE 2021. Springer Proceedings in Mathematics & Statistics, vol 398. Springer, Cham. https://doi.org/10.1007/978-3-031-12766-3_3

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