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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2018.3
- 수록면
- 513 - 521 (9page)
- DOI
- 10.7465/jkdi.2018.29.2.513
이용수
초록· 키워드
오류제보하기
Kendall"s τ is the important association measure and commonly used in biomedical application. Under censoring the estimation of τ has been challenging. Recently Hsieh (2010) replaced a censored event-time by a imputation with the conditional information and calculated the τ estimator based on the imputed data. We propose modified Hsieh"s approach of the Kendall"s tau statistic in the presence of univariate right censoring by implementing with the nonparametric bivariate survival function estimators in Lin and Ying (1993) and Wang and Wells (1997) with good performance under univariate (right)-censoring. Through simulation study the proposed estimators are compared with other practical estimators ^τMO (Oakes, 2008) and ^τLRB (Lakhal et al:, 2009). In the case of data under heavy censoring or with weak association, the modified Hsieh estimators show better performance compared to ^τLRB and ^τMO and seem comparable in other cases of the simulation. An illustrative analysis is also given.
목차
Abstract
1. Introduction
2. Previous estimators of Kendall“s tau under censoring
3. Modified Hsieh“s approach for univariate-censored data
4. Numerical studies
5. Discussion
References
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