Binary quantile regression and variable selection: A new approach
提出一种新的二元分位数回归与变量选择估计方法，通过迭代加权最小二乘实现，计算简单且收敛唯一，蒙特卡洛实验和通勤方式选择数据集验证了其有限样本表现。
In this paper, we propose a new estimation method for binary quantile regression and variable selection which can be implemented by an iteratively reweighted least square approach. In contrast to existing approaches, this method is computationally simple, guaranteed to converge to a unique solution and implemented with standard software packages. We demonstrate our methods using Monte-Carlo experiments and then we apply the proposed method to the widely used work trip mode choice dataset. The results indicate that the proposed estimators work well in finite samples.