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A Note on Robust Estimation of the Extremal Index

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Nonparametric Statistics (ISNPS 2018)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 339))

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Abstract

Many examples in the most diverse fields of application show the need for statistical methods of analysis of extremes of dependent data. A crucial issue that appears when there is dependency is the reliable estimation of the extremal index (EI), a parameter related to the clustering of large events. The most popular EI-estimators, like the blocks’ EI-estimators, are very sensitive to anomalous cluster sizes and exhibit a high bias. The need for robust versions of such EI-estimators is the main topic under discussion in this paper.

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Acknowledgements

Research partially supported by National Funds through Fundação para a Ciência e a Tecnologia (FCT), within projects UID/MAT/00006/2019 (CEA/UL) and UID/MAT/04106/2019 (CIDMA).

Author information

Authors and Affiliations

  1. CEAUL and DEIO, Faculdade de Ciências, Universidade de Lisboa, Lisboa, Portugal

    M. Ivette Gomes

  2. ISCA and CIDMA, Universidade de Aveiro, Aveiro, Portugal

    Miranda Cristina

  3. CIDMA, Universidade de Aveiro, Aveiro, Portugal

    Manuela Souto de Miranda

Authors
  1. M. Ivette Gomes
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  2. Miranda Cristina
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  3. Manuela Souto de Miranda
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Corresponding author

Correspondence to M. Ivette Gomes .

Editor information

Editors and Affiliations

  1. Department of Economics and Statistics, University of Salerno, Salerno, Italy

    Michele La Rocca

  2. Department of Methods and Models for Economics, Terrority and Finance, Sapienza University of Rome, Rome, Italy

    Brunero Liseo

  3. Department of Management and Engineering, University of Padua, Vicenza, Italy

    Luigi Salmaso

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Gomes, M.I., Cristina, M., Souto de Miranda, M. (2020). A Note on Robust Estimation of the Extremal Index. In: La Rocca, M., Liseo, B., Salmaso, L. (eds) Nonparametric Statistics. ISNPS 2018. Springer Proceedings in Mathematics & Statistics, vol 339. Springer, Cham. https://doi.org/10.1007/978-3-030-57306-5_20

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