Different EDF goodness-of-fit tests for competing risks models

The common used goodeness-of-fit tests are based on the empirical distributions functions (EDF) where distances between empirical and theoretical hypothesized distributions are compared to critical values. The aim of this paper is to provide for different sample sizes, tables of goodness-of-fit critical values of modified Kolmogorov-Smirnov statistic Dn, Anderson-Darling statistic A(2), Cramer-Von Mises statistic W2, Liao and Shimokawa statistic L-n, and Watson statistic U-2 for the competing risks model of Bertholon which is used to describe the reliability of real systems where failure times can have different risks and in medical studies to characterize the survival time of patients who can have risks of death from different causes. The power of these statistics is studied using some alternatives such as the exponential, the inverse Weibull, the exponentiated Weibull and the exponentiated exponential distributions. All the computation are carried out by using matlab software and Monte Carlo method.

Automated summary

This paper provides tables of modified goodness-of-fit critical values for different sample sizes, which are used in evaluating the fit of the competing risks model of Bertholon in describing the reliability of real systems and the survival time of patients in medical studies.