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Testing Log Normality for Randomly Censored Data

임의중도절단자료에 대한 로그정규성 검정

  • 김남현 (홍익대학교 기초과학과)
  • Received : 20110600
  • Accepted : 20110900
  • Published : 2011.10.31

Abstract

For survival data we sometimes want to test a log normality hypothesis that can be changed into normality by transforming the survival data. Hence the Shapiro-Wilk type statistic for normality is generalized to randomly censored data based on the Kaplan-Meier product limit estimate of the distribution function. Koziol and Green (1976) derived Cram$\acute{e}$r-von Mises statistic's randomly censored version under the simpl hypothesis. These two test statistics are compared through a simulation study. As for the distribution of censoring variables, we consider Koziol and Green (1976)'s model and other similar models. Through the simulation results, we can see that the power of the proposed statistic is higher than that of Koziol-Green statistic and that the proportion of the censored observations (rather than the distribution of censoring variables) has a strong influence on the power of the proposed statistic.

Keywords

Goodness of fit;random censorship;Kaplan-Meier product limit estimate

Acknowledgement

Supported by : 한국연구재단

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