Testing Exponentiality Based on EDF Statistics for Randomly Censored Data when the Scale Parameter is Unknown

Title & Authors
Testing Exponentiality Based on EDF Statistics for Randomly Censored Data when the Scale Parameter is Unknown
Kim, Nam-Hyun;

Abstract
The simplest and the most important distribution in survival analysis is exponential distribution. Koziol and Green (1976) derived Cram$\small{\acute{e}}$r-von Mises statistic's randomly censored version based on the Kaplan-Meier product limit estimate of the distribution function; however, it could not be practical for a real data set since the statistic is for testing a simple goodness of fit hypothesis. We generalized it to the composite hypothesis for exponentiality with an unknown scale parameter. We also considered the classical Kolmogorov-Smirnov statistic and generalized it by the exact same way. The two statistics are compared through a simulation study. As a result, we can see that the generalized Koziol-Green statistic has better power in most of the alternative distributions considered.
Keywords
Goodness of fit;random censorship;Cram$\small{\acute{e}}$r-von Mises statistic;Kolmogorov-Smirnov statistic;Kaplan-Meier product limit estimate;
Language
Korean
Cited by
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