有序数据的贝叶斯分位数回归

Bayesian Quantile Regression Associated with the Ordinal Data

摘要: 对于一般的分位数回归模型，基于非对称拉普拉斯分布提出了关于有序数据的贝叶斯推理框架。指出了非对称分布的尺度参数在估计中应该被参数化。给出选择尺度参数与模型参数的先验分布的条件，其后验分布是真实概率分布，并采用吉布斯抽样法与马尔卡夫蒙特卡洛模拟方法进行参数估计。

Abstract: In this paper, we introduce an ordinal Bayesian quantile regression model associated with the ordinal data based on asymmetric Laplace distribution. We show that the posterior distributions of estimated parameters always proper when the prior distributions are given, and we also give an efficient Gibbs sampling algorithm for fitting the model to such data. To illustrate this approach, we give a simulation and a real data example.