Binary quantile regression: a Bayesian approach based on the asymmetric Laplace distribution
针对二元响应数据的分位数回归，提出一种基于非对称拉普拉斯分布的贝叶斯方法，解决了频率学派在优化和推断上的困难，并通过蒙特卡洛实验和通勤方式选择数据集验证了其适用性。
SUMMARY This paper develops a Bayesian method for quantile regression for dichotomous response data. The frequentist approach to this type of regression has proven problematic in both optimizing the objective function and making inferences on the parameters. By accepting additional distributional assumptions on the error terms, the Bayesian method proposed sets the problem in a parametric framework in which these problems are avoided. To test the applicability of the method, we ran two Monte Carlo experiments and applied it to Horowitz's (1993) often studied work‐trip mode choice dataset. Compared to previous estimates for the latter dataset, the method proposed leads to a different economic interpretation. Copyright © 2010 John Wiley & Sons, Ltd.