Linear Models, Random Censoring and Synthetic Data
- 1 June 1987
- journal article
- research article
- Published by JSTOR in Biometrika
- Vol. 74 (2) , 301-309
- https://doi.org/10.2307/2336144
Abstract
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).Keywords
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