Gibbs sampling methods for Bayesian quantile regression
Gibbs sampling methods for Bayesian quantile regression
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DOI：
10.1080/00949655.2010.496117
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发表时间：
2011-01-01
影响因子：
1.2
通讯作者：
Kobayashi, Genya
中科院分区：
文献类型：
--
作者：
Kozumi, Hideo;Kobayashi, Genya
本文从贝叶斯的角度考虑了非对称拉普拉斯分布的分位数回归模型。我们开发了一种简单有效的Gibbs抽样算法，用于拟合基于非对称拉普拉斯分布的位置尺度混合表示的分位数回归模型。结果表明，所得到的吉布斯采样器既可以从正态分布采样，也可以从广义逆高斯分布采样。我们还讨论了我们方法的一些可能的扩展，包括尺度参数的结合，双指数先验的使用，以及Tobit分位数回归的贝叶斯分析。通过仿真和实际数据对所提出的方法进行了验证。
This paper considers quantile regression models using an asymmetric Laplace distribution from a Bayesian point of view. We develop a simple and efficient Gibbs sampling algorithm for fitting the quantile regression model based on a location-scale mixture representation of the asymmetric Laplace distribution. It is shown that the resulting Gibbs sampler can be accomplished by sampling from either normal or generalized inverse Gaussian distribution. We also discuss some possible extensions of our approach, including the incorporation of a scale parameter, the use of double exponential prior, and a Bayesian analysis of Tobit quantile regression. The proposed methods are illustrated by both simulated and real data.