LINEAR-MODELS, RANDOM CENSORING AND SYNTHETIC DATA
成果类型：
Article
署名作者：
LEURGANS, S
署名单位：
University System of Ohio; Ohio State University
刊物名称：
BIOMETRIKA
ISSN/ISSBN：
0006-3444
DOI：
10.2307/2336144
发表日期：
1987
页码：
301309
关键词：
摘要：
Estimators for the linear model in the presence of censoring are available. A new extension of the least-squares estimator to censored data is equivalent to applying the ordinary least-squares estimator to synthetic times, time constructed by magnifying the gaps between successive order statistics. Undr suitable regularity conditions, the synthetic data estimator is Fisher consistent and asymptotically normal. Examples facilitate comparison of the synthetic data estimator with estimators proposed by Buckley and James (1979) and by Koul, Susarla and Van Ryzin (1981).