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
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author Reghenzani, Federico
Massari, Giuseppe
Santinelli, Luca
Fornaciari, William
author_facet Reghenzani, Federico
Massari, Giuseppe
Santinelli, Luca
Fornaciari, William
author_sort Reghenzani, Federico
collection PubMed
description 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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spelling pubmed-65622282019-06-17 Statistical power estimation dataset for external validation GoF tests on EVT distribution Reghenzani, Federico Massari, Giuseppe Santinelli, Luca Fornaciari, William Data Brief Mathematics 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. Elsevier 2019-05-28 /pmc/articles/PMC6562228/ /pubmed/31211211 http://dx.doi.org/10.1016/j.dib.2019.104071 Text en © 2019 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Mathematics
Reghenzani, Federico
Massari, Giuseppe
Santinelli, Luca
Fornaciari, William
Statistical power estimation dataset for external validation GoF tests on EVT distribution
title Statistical power estimation dataset for external validation GoF tests on EVT distribution
title_full Statistical power estimation dataset for external validation GoF tests on EVT distribution
title_fullStr Statistical power estimation dataset for external validation GoF tests on EVT distribution
title_full_unstemmed Statistical power estimation dataset for external validation GoF tests on EVT distribution
title_short Statistical power estimation dataset for external validation GoF tests on EVT distribution
title_sort statistical power estimation dataset for external validation gof tests on evt distribution
topic Mathematics
url 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
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