Improving the computation of censored quantile regressions

Censored quantile regressions (CQR) are a valuable tool in economics and engineering. The computation of estimators is highly complex and the performance of standard methods is not satisfactory, in particular if a high degree of censoring is present. Due to an interpolation property the computation of CQR estimates corresponds to the solution of a large scale discrete optimization problem. This feature motivates the use of the global optimization heuristic threshold accepting (TA) in comparison to other algorithms. Simulation results presented in this paper indicate that it can improve finding the exact CQR estimator considerably though it uses more computing time.