Likelihood inference for generalized Pareto distribution

Joan Del Castillo; Isabel Serra

doi:https://doi.org/10.1016/j.csda.2014.10.014

Likelihood inference for generalized Pareto distribution

Joan Del Castillo, Isabel Serra

Research output: Contribution to journal › Article › Research › peer-review

23 Citations (Scopus)

Abstract

© 2014 Elsevier B.V. All rights reserved. A new methodological approach that enables the use of the maximum likelihood method in the Generalized Pareto Distribution is presented. Thus several models for the same data can be compared under Akaike and Bayesian information criteria. The view is based on a detailed theoretical study of the Generalized Pareto Distribution submodels with compact support.

Original language	English
Pages (from-to)	116-128
Journal	Computational Statistics and Data Analysis
Volume	83
DOIs	https://doi.org/10.1016/j.csda.2014.10.014
Publication status	Published - 1 Jan 2015

Keywords

Akaike and Bayesian information criteria
Bilbao data
Extreme value theory
Maximum likelihood estimation
Model selection

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https://doi.org/10.1016/j.csda.2014.10.014

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title = "Likelihood inference for generalized Pareto distribution",

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author = "Castillo, {Joan Del} and Isabel Serra",

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AU - Serra, Isabel

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AB - © 2014 Elsevier B.V. All rights reserved. A new methodological approach that enables the use of the maximum likelihood method in the Generalized Pareto Distribution is presented. Thus several models for the same data can be compared under Akaike and Bayesian information criteria. The view is based on a detailed theoretical study of the Generalized Pareto Distribution submodels with compact support.

KW - Akaike and Bayesian information criteria

KW - Bilbao data

KW - Extreme value theory

KW - Maximum likelihood estimation

KW - Model selection

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DO - https://doi.org/10.1016/j.csda.2014.10.014

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EP - 128

JO - Computational Statistics and Data Analysis

JF - Computational Statistics and Data Analysis

SN - 0167-9473

ER -

Likelihood inference for generalized Pareto distribution

Abstract

Keywords

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