Hierarchical models for repeated binary data using the IBF sampler

DOI10.1016/j.csda.2004.12.006zbMath1432.62231OpenAlexW1969355378MaRDI QIDQ959226

Ming Tan, Guo-Liang Tian, Kai Wang Ng

Publication date: 11 December 2008

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2004.12.006

zbMATH Keywords

Gibbs samplerMCMCBayesian computationMonte Carlo EM algorithminverse Bayes formulae

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Generalized linear models (logistic models) (62J12)

Related Items (7)

Analysis of clustered spatially correlated binary data using autologistic model and Bayesian method with an application to dental caries of 3–5-year-old children ⋮ Bayesian spatial prediction of skew and censored data via a hybrid algorithm ⋮ A model for analyzing spatially correlated binary data clustered in uncorrelated lattices ⋮ Bayesian analysis of two dependent \(2\times 2\) contingency tables ⋮ An exact non‐iterative sampling procedure for discrete missing data problems ⋮ Bayesian analysis of multivariatetlinear mixed models with missing responses at random ⋮ Bayesian analysis of multivariate t linear mixed models using a combination of IBF and Gibbs samplers

Uses Software

• BayesDA

Cites Work

• Random-Effects Models for Longitudinal Data
• Improving generalised estimating equations using quadratic inference functions
• Perfect Slice Samplers
• The Calculation of Posterior Distributions by Data Augmentation
• Analysis of multivariate probit models
• Consistent Estimators in Generalized Linear Mixed Models
• Parameter expansion to accelerate EM: the PX-EM algorithm
• Direct Calculation of the Information Matrix via the EM Algorithm
• Maximizing Generalized Linear Mixed Model Likelihoods With an Automated Monte Carlo EM Algorithm
• Maximum Likelihood Variance Components Estimation for Binary Data
• Bias Correction in Generalized Linear Mixed Models With Multiple Components of Dispersion
• Maximum Likelihood Algorithms for Generalized Linear Mixed Models
• Model diagnostics for marginal regression analysis of correlated binary data
• Approximate Inference in Generalized Linear Mixed Models
• A likelihood-based method for analysing longitudinal binary responses
• Improved Sampling‐Importance Resampling and Reduced Bias Importance Sampling
• Monte Carlo EM Estimation for Time Series Models Involving Counts
• Bias correction in generalised linear mixed models with a single component of dispersion
• Tools for statistical inference. Methods for the exploration of posterior distributions and likelihood functions.
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