Partially Collapsed Gibbs Sampling for Linear Mixed-effects Models

DOI10.1080/03610918.2013.857687zbMath1384.62276OpenAlexW2052041811MaRDI QIDQ2809592

Seunghyun Min, Taeyoung Park

Publication date: 30 May 2016

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2013.857687

zbMATH Keywords

Bayesian analysisMarkov chain Monte CarloblockingGibbs samplingcollapsingjump diffusion

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Linear inference, regression (62J99) Monte Carlo methods (65C05)

Related Items (10)

A robustness evaluation of Bayesian tests for longitudinal data ⋮ Analysis of binary longitudinal data with time-varying effects ⋮ Simulating conditionally specified models ⋮ Semiparametric Bayesian inference for mean-covariance regression models ⋮ Sampling hyperparameters in hierarchical models: Improving on Gibbs for high-dimensional latent fields and large datasets ⋮ Functional clustering methods for binary longitudinal data with temporal heterogeneity ⋮ Analysis of Poisson varying-coefficient models with autoregression ⋮ Efficient Bayesian analysis of multivariate aggregate choices ⋮ Efficient and flexible model-based clustering of jumps in diffusion processes ⋮ Bayesian variable selection in Poisson change-point regression analysis

Cites Work

• Random-Effects Models for Longitudinal Data
• Extra-Binomial Variation in Logistic Linear Models
• Inference from iterative simulation using multiple sequences
• Partially Collapsed Gibbs Samplers
• Seeking efficient data augmentation schemes via conditional and marginal augmentation
• Covariance structure of the Gibbs sampler with applications to the comparisons of estimators and augmentation schemes
• Option pricing when underlying stock returns are discontinuous

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