变系数异方差模型的贝叶斯分析

Bayesian Analysis of Varying Coefficient Heteroscedastic Models

摘要: 基于方差建模研究了变系数异方差模型的贝叶斯估计和异常点识别，其中非参数部分采用B样条逼近。主要通过应用Gibbs抽样和Metropolis-Hastings算法相结合的混合算法获得模型的贝叶斯估计和通过K-L距离贝叶斯诊断统计量来识别数据异常点。模拟研究显示所提出的贝叶斯分析方法是可行有效的。

Abstract: Based on variance modeling, Bayesian estimation and outlier identification of varying coefficient heteroscedastic models are studied, where the nonparametric part is approximated by B-spline. By combining the Gibbs sampler and Metropolis-Hastings algorithm, Bayesian estimation and Bayesian diagnosis statistics based on the K-L distance are obtained to identify outliers. Simulation studies show that the proposed Bayesian methods are feasible and effective.