Semiparametric Bayesian Inference for Mean-Covariance Regression Models

In this paper, we propose a Bayesian semiparametric mean-covariance regression model with known covariance structures. A mixture model is used to describe the potential non-normal distribution of the regression errors. Moreover, an empirical likelihood adjusted mixture of Dirichlet process model is...

Full description

Saved in:
Bibliographic Details
Published in:Acta mathematica Sinica. English series 2017-06, Vol.33 (6), p.748-760
Main Authors: Yu, Han Jun, Shen, Jun Shan, Li, Zhao Nan, Fang, Xiang Zhong
Format: Article
Language:English
Subjects:
Bayesian analysis
Computer simulation
Constraint modelling
Covariance
Dirichlet
Dirichlet problem
Empirical analysis
Mathematics
Mathematics and Statistics
Nonparametric statistics
Normal distribution
Regression analysis
Regression models
Sleep
Sleep deprivation
Statistical inference
半参数
协方差结构
回归模型
回归误差
均值
贝叶斯推断
非正态分布
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, we propose a Bayesian semiparametric mean-covariance regression model with known covariance structures. A mixture model is used to describe the potential non-normal distribution of the regression errors. Moreover, an empirical likelihood adjusted mixture of Dirichlet process model is constructed to produce distributions with given mean and variance constraints. We illustrate through simulation studies that the proposed method provides better estimations in some non-normal cases. We also demonstrate the implementation of our method by analyzing the data set from a sleep deprivation study.
ISSN:1439-8516
1439-7617
DOI:10.1007/s10114-016-6357-7