Maximum weighted likelihood for discrete choice models with a dependently censored covariate

This study considers discrete choice models with a censored covariate under dependent censoring where the censoring mechanism depends on the outcomes of choice models. We estimate the parameter vector using maximum weighted likelihood (MWL). The weights are obtained through the Aalen's estimator. Our estimator for the parameter vector in choice models is consistent and asymptotically normal. Simulations show that MWL performs well. Finally, the proposed MWL method is applied to a real data set.