An Iterative Weighted Least Squares Algorithm and Simulation Study for Censored Data M-Estimates

A popular linear regression estimator for censored data is the one proposed by Buckley and James (1979). However, this estimator is not robust to outliers, which is not surprising since it is a modified version of the uncensored data least squares estimator. Lai and Ying (1994) have proposed an M-estimator for censored data that is a generalization of the Buckley- James estimator. In this paper we discuss a weighted least squares algorithm for computing these M-estimates and compare the performance of two Huber M-estimators with the Buckley-James estimator in a simulation study. We find that the Huber M-estimators perform more robustly for a broad range of censoring and error distributions.