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

척도모수가 미지인 임의중도절단자료의 EDF 통계량을 이용한 지수 검정

  • 김남현 (홍익대학교 기초과학과)
  • Received : 2012.01.02
  • Accepted : 2012.03.09
  • Published : 2012.04.30

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

Acknowledgement

Supported by : 한국연구재단

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