标题： 贝叶斯分位数回归在有序纵向数据中的应用 显示英文标题

标题： Bayesian Quantile Regression for Ordinal Longitudinal Data

摘要： 自Koenker和Bassett（1978）的开创性工作以来，分位数回归模型及其应用在许多领域中的研究变得越来越流行和重要。 本文提出了一种随机效应有序分位数回归模型，用于分析具有有序结果的纵向数据。 基于偏斜双指数分布的位置尺度混合表示，推导出了一种有效的Gibbs采样算法来拟合该模型到数据。 所提出的方法通过模拟数据和真实数据实例进行了说明。 这是首次讨论用于分析具有有序结果的纵向数据的分位数回归的工作。 显示英文摘要

摘要： Since the pioneering work by Koenker and Bassett (1978), quantile regression models and its applications have become increasingly popular and important for research in many areas. In this paper, a random effects ordinal quantile regression model is proposed for analysis of longitudinal data with ordinal outcome of interest. An efficient Gibbs sampling algorithm was derived for fitting the model to the data based on a location scale mixture representation of the skewed double exponential distribution. The proposed approach is illustrated using simulated data and a real data example. This is the first work to discuss quantile regression for analysis of longitudinal data with ordinal outcome.