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

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, h...

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Detalles Bibliográficos
Autores principales: Reghenzani, Federico, Massari, Giuseppe, Santinelli, Luca, Fornaciari, William
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Mathematics
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6562228/
https://www.ncbi.nlm.nih.gov/pubmed/31211211
http://dx.doi.org/10.1016/j.dib.2019.104071
Descripción
Sumario: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.

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