A goodness-of-fit test for heavy tailed distributions with unknown parameters and its application to simulated precipitation extremes in the Euro-Mediterranean region

G. Jogesh Babu, Andrea Toreti

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

We establish a general bootstrap procedure combined with a modified Anderson-Darling statistic. This procedure is proved to be valid for heavy tailed generalized Pareto distributions that are commonly used to model excesses over a high threshold in extreme value theory. Then, the method is applied to daily precipitation excesses simulated over the Euro-Mediterranean region in autumn by four regional climate models from the EURO-CORDEX initiative.

Original languageEnglish (US)
Pages (from-to)11-19
Number of pages9
JournalJournal of Statistical Planning and Inference
Volume174
DOIs
StatePublished - Jan 1 2016

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Climate models
Heavy-tailed Distribution
Goodness of Fit Test
Unknown Parameters
Excess
Extremes
Statistics
Generalized Pareto Distribution
Extreme Value Theory
Climate Models
Bootstrap
Statistic
Valid
Goodness of fit test
Heavy-tailed distribution
Model
Extreme value theory
Climate
Generalized Pareto distribution

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Cite this

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abstract = "We establish a general bootstrap procedure combined with a modified Anderson-Darling statistic. This procedure is proved to be valid for heavy tailed generalized Pareto distributions that are commonly used to model excesses over a high threshold in extreme value theory. Then, the method is applied to daily precipitation excesses simulated over the Euro-Mediterranean region in autumn by four regional climate models from the EURO-CORDEX initiative.",
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