Nonconjugate Bayesian Analysis of Variance Component Models

DOI10.1111/j.0006-341X.2000.00768.xzbMath1060.62531OpenAlexW2073566774WikidataQ52074173 ScholiaQ52074173MaRDI QIDQ4670410

Robert E. Kass, Russell D. Wolfinger

Publication date: 22 April 2005

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.0006-341x.2000.00768.x

zbMATH Keywords

Jeffreys' priorreference priormixed modelREMLposterior simulationindependence chain

Mathematics Subject Classification ID

Bayesian inference (62F15) Analysis of variance and covariance (ANOVA) (62J10)

Related Items (6)

Computation of reference Bayesian inference for variance components in longitudinal studies ⋮ Exact prior-free probabilistic inference on the heritability coefficient in a linear mixed model ⋮ Bayesian tolerance intervals for the balanced two-factor nested random effects model ⋮ Generalized fiducial inference for normal linear mixed models ⋮ A Hybrid Approximation Bayesian Test of Variance Components for Longitudinal Data ⋮ On propriety of posterior distributions of variance components in small area estimation

Uses Software

• SAS
• SAS/STAT

Cites Work

• Unnamed Item
• Simulation-based Bayesian inferences for two-variance components linear models
• Markov chains for exploring posterior distributions. (With discussion)
• Bayesian analysis for random coefficient regression models using noninformative priors
• Bayesian Methods for Variance Component Models
• On Non-Negative Quadratic Unbiased Estimation of Variance Components
• Restricted Maximum Likelihood (REML) Estimation of Variance Components in the Mixed Model
• Computing Gaussian Likelihoods and Their Derivatives for General Linear Mixed Models
• The Selection of Prior Distributions by Formal Rules
• Hierarchical Poisson Regression Modeling
• Reference Bayesian Methods for Generalized Linear Mixed Models
• Bayesian inference for variance components using only error contrasts
• Efficient parametrisations for normal linear mixed models
• A prior for the variance in hierarchical models
• Recovery of inter-block information when block sizes are unequal
• Estimation of Variance and Covariance Components in Linear Models

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