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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국통계학회 CSAM(Communications for Statistical Applications and Methods) CSAM(Communications for Statistical Applications and Methods) 제26권 제5호
- 발행연도
- 2019.9
- 수록면
- 431 - 443 (13page)
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초록· 키워드
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Goodness-of-fit techniques are an important topic in statistical analysis. Censored data occur frequently in survival experiments; therefore, many studies are conducted when data are censored. In this paper we mainly consider test statistics based on the empirical distribution function (EDF) to test normal distributions with unknown location and scale parameters when data are randomly censored. The most famous EDF test statistic is the Kolmogorov-Smirnov; in addition, the quadratic statistics such as the Cram´er-von Mises and the Anderson-Darling statistic are well known. The Cram´er-von Mises statistic is generalized to randomly censored cases by Koziol and Green (Biometrika, 63, 465–474, 1976). In this paper, we generalize the Anderson-Darling statistic to randomly censored data using the Kaplan-Meier estimator as it was done by Koziol and Green. A simulation study is conducted under a particular censorship model proposed by Koziol and Green. Through a simulation study, the generalized Anderson-Darling statistic shows the best power against almost all alternatives considered among the three EDF statistics we take into account.
목차
Abstract
1. Introduction
2. Goodness-of-fit test statistics
3. Simulation and examples
4. Concluding remarks
References
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