A goodness of fit test for the Pareto distribution with progressively type II censored data based on the cumulative hazard function

Abstract In this article, we develop a goodness-of-fit test process for Pareto distribution with two parameters based on progressive Type II right censoring data. Based on data transformation and the non-parametric estimation of the hazard function, a goodness-of-fittest statistic is constructed. The empirical distribution of the test statistics is obtained. The distribution of the test statistics is independent of the selection of the parameters but related to the censoring percentage. Using the Monte Carlo simulation, compare the power of the proposed test statistic with that of the test statistics T 1 in Baratpour and Rad (2016) [1] and U in Park and Pakyari (2015) with monotonic and non-monotonic hazard functions. We find that the test statistics perform better when the sample size is large and the hazard function is monotonic. For non-monotonic hazard functions, the statistic r h s also has a good performance when the sample size is large and the censored scheme is uniform. Finally, three examples are given to illustrate the proposed test.