Semiparametric EM-estimation of censored linear regressionmodels for durations

This paper investigates the sensitivity of maximum quasi likelihood estimators of the covariate eeects in duration models in the presence of misspeciication due to neglected heterogeneity or misspeciication of the hazard function. We consider linear models for r (T) where T is duration and r is a known, strictly increasing function. This class of models is also referred to as location-scale models. In the absence of censoring, Gould and Lawless (1988) have shown that maximum likelihood estimators of the regression parameters are consistent and asymptotically normally distributed under the assumption that the location-scale structure of the model is of the correct form. In the presence of censoring, however, model misspeciication leads to inconsistent estimates of the regression coeecients for most of the censoring mechanisms that are widely used in practice. We propose a semiparametric EM-estimator, following ideas of Ritov (1990), and Buckley and James (1979). This estimator is robust against misspeciication and is highly recommended if there is heavy censoring and if there may be speciication errors. We present the results of simulation experiments illustrating the performance of the proposed estimator.