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

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 pe...

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Published in:arXiv.org 2024-08
Main Authors: Avhad, Ganesh Vishnu, Lahiri, Ananya, Kattumannil, Sudheesh K
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Language:English
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Asymptotic methods
Fixed points (mathematics)
Goodness of fit
Monte Carlo simulation
Statistical tests
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Lahiri, Ananya
Kattumannil, Sudheesh K
description 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.
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subjects Asymptotic methods
Fixed points (mathematics)
Goodness of fit
Monte Carlo simulation
Statistical tests
title Nonparametric goodness of fit tests for Pareto type-I distribution with complete and censored data
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