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Statistical power estimation dataset for external validation GoF tests on EVT distribution.

ABSTRACT: This paper presents the statistical power estimation of goodness-of-fit tests for Extreme Value Theory (EVT) distributions. The presented dataset provides quantitative information on the statistical power, in order to enable the sample size selection in external validation scenario. In particular, high precision estimations of the statistical power of KS, AD, and MAD goodness-of-fit tests have been computed using a Monte Carlo approach. The full raw dataset resulting from this analysis has been published as reference for future studies: https://doi.org/10.17632/hh2byrbbmf.1.

SUBMITTER: Reghenzani F

PROVIDER: S-EPMC6562228 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Statistical power estimation dataset for external validation GoF tests on EVT distribution.

Reghenzani Federico F Massari Giuseppe G Santinelli Luca L Fornaciari William W

Data in brief 20190528

This paper presents the statistical power estimation of goodness-of-fit tests for Extreme Value Theory (EVT) distributions. The presented dataset provides quantitative information on the statistical power, in order to enable the sample size selection in external validation scenario. In particular, high precision estimations of the statistical power of KS, AD, and MAD goodness-of-fit tests have been computed using a Monte Carlo approach. The full raw dataset resulting from this analysis has been ...[more]

PMID: 31211211

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