Winsorized Mean Estimator for Censored Regression

We introduce a semiparametric estimator for the censored linear regression model. It is based on the regression version of Huber's [6] M-estimator. It includes Powell's [19] censored least absolute deviations estimator as a special case and is related to Powell's [20] symmetrically censored least-squares estimator. We prove strong consistency and derive its asymptotic distribution which is √n-consistent with an easily computable covariance matrix. A small-scale simulation study shows that it works quite well in various cases.