Nonparametric goodness of fit tests for Pareto type-I distribution with complete and censored data

Fuente: arXiv
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Autores principales: Vishnu, Avhad Ganesh, Lahiri, Ananya, Kattumannil, Sudheesh K.
Formato: Preprint
Publicado: 2024
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author Vishnu, Avhad Ganesh
Lahiri, Ananya
Kattumannil, Sudheesh K.
author_facet Vishnu, Avhad Ganesh
Lahiri, Ananya
Kattumannil, Sudheesh K.
contents Two new goodness of fit tests for the Pareto type-I distribution for complete and right censored data are proposed using fixed point characterization based on Steins type identity. The asymptotic distributions of the test statistics under both the null and alternative hypotheses are obtained. The performance of the proposed tests is evaluated and compared with existing tests through a Monte Carlo simulation experiment. The newly proposed tests exhibit greater power than existing tests for the Pareto type-I distribution. Finally, the methodology is applied to real-world data sets.
format Preprint
id arxiv_https___arxiv_org_abs_2408_16384
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Nonparametric goodness of fit tests for Pareto type-I distribution with complete and censored data
Vishnu, Avhad Ganesh
Lahiri, Ananya
Kattumannil, Sudheesh K.
Methodology
Statistics Theory
Two new goodness of fit tests for the Pareto type-I distribution for complete and right censored data are proposed using fixed point characterization based on Steins type identity. The asymptotic distributions of the test statistics under both the null and alternative hypotheses are obtained. The performance of the proposed tests is evaluated and compared with existing tests through a Monte Carlo simulation experiment. The newly proposed tests exhibit greater power than existing tests for the Pareto type-I distribution. Finally, the methodology is applied to real-world data sets.
title Nonparametric goodness of fit tests for Pareto type-I distribution with complete and censored data
topic Methodology
Statistics Theory
url https://arxiv.org/abs/2408.16384