A Stochastic EM Algorithm for Quantile and Censored Quantile Regression Models

We proposed a stochastic EM algorithm for quantile and censored quantile regression models in order to circumvent some limitations of the EM algorithm and Gibbs sampler. We conducted several simulation studies to illustrate the performance of the algorithm and found that the procedure performs as better as the Gibbs sampler, and outperforms the EM algorithm in uncensored situation. Finally we applied the methodology to the classical Engel food expenditure data and the labour supply data with left censoring, finding that the SEM algorithm behaves more satisfying than the Gibbs sampler does.