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http://dx.doi.org/10.5351/KJAS.2012.25.2.311

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

Kim, Nam-Hyun (Department of Science, Hongik University)
Publication Information
The Korean Journal of Applied Statistics / v.25, no.2, 2012 , pp. 311-319 More about this Journal
Abstract
The simplest and the most important distribution in survival analysis is exponential distribution. Koziol and Green (1976) derived Cram$\acute{e}$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$\acute{e}$r-von Mises statistic; Kolmogorov-Smirnov statistic; Kaplan-Meier product limit estimate;
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Times Cited By KSCI : 1  (Citation Analysis)
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