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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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 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 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.
 Keywords
Goodness of fit;random censorship;Kaplan-Meier product limit estimate;
 Language
Korean
 Cited by
1.
척도모수가 미지인 임의중도절단자료의 EDF 통계량을 이용한 지수 검정,김남현;

응용통계연구, 2012. vol.25. 2, pp.311-319 crossref(new window)
1.
Testing Exponentiality Based on EDF Statistics for Randomly Censored Data when the Scale Parameter is Unknown, Korean Journal of Applied Statistics, 2012, 25, 2, 311  crossref(new windwow)
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