变系数异方差模型的贝叶斯分析  

Bayesian Analysis of Varying Coefficient Heteroscedastic Models

作　　者：许芳忠 徐登可[1] 

机构地区：[1]浙江农林大学统计系,浙江 杭州

出　　处：《应用数学进展》2020年第12期2166-2175,共10页Advances in Applied Mathematics

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。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.

关 键 词：异方差模型 Metropolis-Hastings算法 贝叶斯估计 K-L距离 B样条 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]