Goodness-of-fit tests for the Weibull and extreme value distributions: a review and comparative study

Meryam Krit; Olivier Gaudoin; Emmanuel Remy

doi:10.1080/03610918.2019.1594292

Goodness-of-fit tests for the Weibull and extreme value distributions: a review and comparative study

Meryam Krit, Olivier Gaudoin, Emmanuel Remy

Research output: Contribution to journal › A1: Web of Science-article › peer-review

Abstract

This paper presents an up-to-date review of Goodness-of-Fit (GoF) tests for the two-parameter Weibull and Extreme Value distributions. This review includes classical tests and more recent ones. An extensive comparison study is presented, which allows to identify the most powerful tests. The comparison is made using the EWGoF R package. Some guidance is given on the selection of the most appropriate tests based on the features of the studied data. Finally, an application to industrial data is presented.

Original language	English
Journal	Communications in Statistics - Simulation and Computation
Volume	50
Issue number	7
Pages (from-to)	1888-1911
ISSN	0361-0918
DOIs	https://doi.org/10.1080/03610918.2019.1594292
Publication status	Published - 2019

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10.1080/03610918.2019.1594292

Cite this

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abstract = "This paper presents an up-to-date review of Goodness-of-Fit (GoF) tests for the two-parameter Weibull and Extreme Value distributions. This review includes classical tests and more recent ones. An extensive comparison study is presented, which allows to identify the most powerful tests. The comparison is made using the EWGoF R package. Some guidance is given on the selection of the most appropriate tests based on the features of the studied data. Finally, an application to industrial data is presented.",

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AB - This paper presents an up-to-date review of Goodness-of-Fit (GoF) tests for the two-parameter Weibull and Extreme Value distributions. This review includes classical tests and more recent ones. An extensive comparison study is presented, which allows to identify the most powerful tests. The comparison is made using the EWGoF R package. Some guidance is given on the selection of the most appropriate tests based on the features of the studied data. Finally, an application to industrial data is presented.

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DO - 10.1080/03610918.2019.1594292

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JO - Communications in Statistics - Simulation and Computation

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