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Testing Log Normality for Randomly Censored Data
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 Title & Authors
Testing Log Normality for Randomly Censored Data
Kim, Nam-Hyun;
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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 Cramr-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.
Goodness of fit;random censorship;Kaplan-Meier product limit estimate;
 Cited by
척도모수가 미지인 임의중도절단자료의 EDF 통계량을 이용한 지수 검정,김남현;

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