Semiparametric Bayesian inference for mean-covariance regression models

DOI10.1007/s10114-016-6357-7zbMath1373.62167OpenAlexW2562737881MaRDI QIDQ2403991

Xiang-Zhong Fang, Han-Jun Yu, Zhao-Nan Li, Jun-shan Shen

Publication date: 12 September 2017

Published in: Acta Mathematica Sinica. English Series (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10114-016-6357-7

zbMATH Keywords

Dirichlet processclustered dataempirical likelihoodnonparametric Bayesmoment constraints

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Linear regression; mixed models (62J05) Parametric inference under constraints (62F30) Bayesian inference (62F15)

Uses Software

• lme4

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Bayesian inference in joint modelling of location and scale parameters of the \(t\) distribution for longitudinal data
• The geometry of mixture likelihoods, part II: The exponential family
• Bayes methods for a symmetric unimodal density and its mode
• The geometry of mixture likelihoods: A general theory
• Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems
• Ferguson distributions via Polya urn schemes
• Nonparametric estimation of convex models via mixtures
• Semiparametric Bayes hierarchical models with mean and variance constraints
• A Bayesian analysis of some nonparametric problems
• Partially Collapsed Gibbs Sampling for Linear Mixed-effects Models
• Empirical likelihood ratio confidence intervals for a single functional
• Nonparametric Maximum Likelihood Estimation of a Mixing Distribution
• Estimating Normal Means with a Dirichlet Process Prior
• On modelling mean-covariance structures in longitudinal studies
• Bayesian Density Estimation and Inference Using Mixtures
• Joint mean-covariance models with applications to longitudinal data: unconstrained parameterisation
• Regression models for covariance structures in longitudinal studies
• Modelling conditional covariance in the linear mixed model
• Prior distributions for variance parameters in hierarchical models (Comment on article by Browne and Draper)