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

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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Main Authors: Federico Reghenzani, Giuseppe Massari, Luca Santinelli, William Fornaciari
Format: Article
Language:English
Published: Elsevier 2019-08-01
Series:Data in Brief
Online Access:http://www.sciencedirect.com/science/article/pii/S2352340919304251
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spelling doaj-57d33a021b064ae0aa0600b28abbad9f2020-11-25T01:33:27ZengElsevierData in Brief2352-34092019-08-0125Statistical power estimation dataset for external validation GoF tests on EVT distributionFederico Reghenzani0Giuseppe Massari1Luca Santinelli2William Fornaciari3DEIB, Politecnico di Milano, Milano, Italy; Corresponding author.DEIB, Politecnico di Milano, Milano, ItalyDTIS, Onera, Toulouse, FranceDEIB, Politecnico di Milano, Milano, ItalyThis 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.http://www.sciencedirect.com/science/article/pii/S2352340919304251
collection DOAJ
language English
format Article
sources DOAJ
author Federico Reghenzani
Giuseppe Massari
Luca Santinelli
William Fornaciari
spellingShingle Federico Reghenzani
Giuseppe Massari
Luca Santinelli
William Fornaciari
Statistical power estimation dataset for external validation GoF tests on EVT distribution
Data in Brief
author_facet Federico Reghenzani
Giuseppe Massari
Luca Santinelli
William Fornaciari
author_sort Federico Reghenzani
title 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_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_sort statistical power estimation dataset for external validation gof tests on evt distribution
publisher Elsevier
series Data in Brief
issn 2352-3409
publishDate 2019-08-01
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.
url http://www.sciencedirect.com/science/article/pii/S2352340919304251
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AT giuseppemassari statisticalpowerestimationdatasetforexternalvalidationgoftestsonevtdistribution
AT lucasantinelli statisticalpowerestimationdatasetforexternalvalidationgoftestsonevtdistribution
AT williamfornaciari statisticalpowerestimationdatasetforexternalvalidationgoftestsonevtdistribution
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