An Alternative Estimator for the Censored Quantile Regression Model
提出一种线性删失分位数回归模型的替代估计量，目标函数全局凸，可通过线性规划求解，在弱条件下具有√n收敛速度和渐近正态性，蒙特卡洛模拟显示小样本性质良好。
The paper introduces an alternative estimator for the linear censored quantile regression model. The objective function is globally convex and the estimator is a solution to a linear programming problem. Hence, a global minimizer is obtained in a finite number of simplex iterations. The suggested estimator also applies to the case where the censoring point is an unknown function of a set of regressors. It is shown that, under fairly weak conditions, the estimator has a √n-convergence rate and is asymptotically normal. In the case of a fixed censoring point, its asymptotic property is nearly equivalent to that of the estimator suggested by Powell (1984, 1986a). A Monte Carlo study performed shows that the suggested estimator has very desirable small sample properties. It precisely corrects for the bias induced by censoring, even when there is a large amount of censoring, and for relatively small sample sizes.