Work

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

Year: 2019

Type: data-paper

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.

Source: Data in Brief

DOI: 10.1016/j.dib.2019.104071

Authors: Federico Reghenzani, Giuseppe Massari, Luca Santinelli, William Fornaciari

Institutions: University of Milan, Politecnico di Milano, Office National d'Études et de Recherches Aérospatiales, Université de Toulouse

Language: English

Topic: Statistical Distribution Estimation and Applications

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $500

Funder: Horizon 2020 Framework Programme