Auxiliary mixture sampling with applications to logistic models
Computational Statistics & Data Analysis
Computational techniques for spatial logistic regression with large data sets
Computational Statistics & Data Analysis
A general approach to heteroscedastic linear regression
Statistics and Computing
Generalized linear mixed model with a penalized Gaussian mixture as a random effects distribution
Computational Statistics & Data Analysis
Marginal likelihoods for non-Gaussian models using auxiliary mixture sampling
Computational Statistics & Data Analysis
Parametrization and penalties in spline models with an application to survival analysis
Computational Statistics & Data Analysis
Bayesian density estimation from grouped continuous data
Computational Statistics & Data Analysis
Locally adaptive Bayesian P-splines with a Normal-Exponential-Gamma prior
Computational Statistics & Data Analysis
Generalized structured additive regression based on Bayesian P-splines
Computational Statistics & Data Analysis
A sign based loss approach to model selection in nonparametric regression
Statistics and Computing
Choice of generalized linear mixed models using predictive crossvalidation
Computational Statistics & Data Analysis
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Generalized linear mixed models provide a unified framework for treatment of exponential family regression models, overdispersed data and longitudinal studies. These problems typically involve the presence of random effects and this paper presents a new methodology for making Bayesian inference about them. The approach is simulation-based and involves the use of Markov chain Monte Carlo techniques. The usual iterative weighted least squares algorithm is extended to include a sampling step based on the Metropolis–Hastings algorithm thus providing a unified iterative scheme. Non-normal prior distributions for the regression coefficients and for the random effects distribution are considered. Random effect structures with nesting required by longitudinal studies are also considered. Particular interests concern the significance of regression coefficients and assessment of the form of the random effects. Extensions to unknown scale parameters, unknown link functions, survival and frailty models are outlined.