Exponentiated generalized Pareto distribution: Properties and applications towards extreme value theory

Seyoon Lee, Joseph H.T. Kim

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

The GPD is a central distribution in modelling heavy tails in many applications. Applying the GPD to actual datasets however is not trivial. In this paper we propose the Exponentiated GPD (exGPD), created via log-transform of the GPD variable, which has less sample variability. Various distributional quantities of the exGPD are derived analytically. As an application we also propose a new plot based on the exGPD as an alternative to the Hill plot to identify the tail index of heavy tailed datasets, and carry out simulation studies to compare the two.

Original languageEnglish
Pages (from-to)2014-2038
Number of pages25
JournalCommunications in Statistics - Theory and Methods
Volume48
Issue number8
DOIs
Publication statusPublished - 2019 Apr 18

Bibliographical note

Funding Information:
This research is supported by Basic Science Research Program of the National Research Foundation of Korea (NRF-2015R1A1A1A05027336)

Publisher Copyright:
© 2018, © 2018 Taylor & Francis Group, LLC.

All Science Journal Classification (ASJC) codes

  • Statistics and Probability

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