论文标题
贝叶斯分位数回归用于序数模型
Bayesian Quantile Regression for Ordinal Models
论文作者
论文摘要
本文引入了单变量序数模型中分位数回归的贝叶斯估计方法。提出了两种算法，该算法利用了Albert和Chib（1993）的潜在变量推理框架以及不对称拉普拉斯分布的正常指数混合物表示。估计利用了马尔可夫链蒙特卡洛模拟 - 吉布斯与大都市束缚算法一起采样或仅吉布斯采样。这些算法用于两项模拟研究中，并在经济学问题（教育程度）和政治经济学（有关扩大“布什税”削减的公众舆论）的分析中实施。对模型比较的研究例证了分位数序数模型的实际实用性。
The paper introduces a Bayesian estimation method for quantile regression in univariate ordinal models. Two algorithms are presented that utilize the latent variable inferential framework of Albert and Chib (1993) and the normal-exponential mixture representation of the asymmetric Laplace distribution. Estimation utilizes Markov chain Monte Carlo simulation - either Gibbs sampling together with the Metropolis-Hastings algorithm or only Gibbs sampling. The algorithms are employed in two simulation studies and implemented in the analysis of problems in economics (educational attainment) and political economy (public opinion on extending "Bush Tax" cuts). Investigations into model comparison exemplify the practical utility of quantile ordinal models.
