Linear models, random censoring and synthetic data

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. Under suitable regularity conditions, the synthetic data estimator is Fisher consistent and asymptotically normal. Examples facilitate com parison of the synthetic data estimator with estimators proposed by Buckley & James (1979) and by Koul, Susarla & Van Ryzin (1981).