Bayesian model selection for logistic regression models with random intercept

• Published in: Computational statistics & data analysis Vol. 56; no. 5; pp. 1256 - 1274
• Main Authors: Wagner, Helga, Duller, Christine
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2012; Elsevier
• Series: Computational Statistics & Data Analysis
• Subjects: Auxiliary mixture sampling; Bayesian analysis; Heterogeneity; Logistics; Mathematical models; MCMC; Normal scale mixtures; Regression; Risk; Specifications; Spike and slab priors; Statistics; Variable selection; Variable selection Variance selection MCMC Auxiliary mixture sampling Normal scale mixtures Spike and slab priors; Variance selection; MCMC; Variance selection; Auxiliary mixture sampling; Normal scale mixtures; Spike and slab priors; Variable selection

Data, collected to model risk of an interesting event, often have a multilevel structure as patients are clustered within larger units, e.g. clinical centers. Risk of the event is usually modeled using a logistic regression model, with a random intercept to control for heterogeneity among clusters....