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

TL;DR

This paper introduces two novel goodness of fit tests for Pareto type-I distribution applicable to complete and censored data, demonstrating superior power through simulations and real data applications.

Contribution

It proposes new tests based on Stein's identity for Pareto type-I distribution, extending goodness of fit testing to censored data with improved performance.

Findings

Proposed tests have greater power than existing methods.

Asymptotic distributions are derived under null and alternative hypotheses.

Tests successfully applied to real-world data sets.

Abstract

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.