Adaptive Sparseness for Supervised Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian parsimonious covariance estimation for hierarchical linear mixed models
Statistics and Computing
Generalized structured additive regression based on Bayesian P-splines
Computational Statistics & Data Analysis
Bayesian estimation of random effects models for multivariate responses of mixed data
Computational Statistics & Data Analysis
Editorial: Second Issue for Computational Statistics for Clinical Research
Computational Statistics & Data Analysis
Efficient MCMC for Binomial Logit Models
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special Issue on Monte Carlo Methods in Statistics
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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. Model specification requires to decide which regressors have a non-negligible effect, and hence, should be included in the final model and whether risk is actually heterogeneous among centers, i.e. whether the model should include a random intercept or not. In a Bayesian approach, these questions can be answered by combining variable selection with variance selection of the random intercept. Bayesian model selection is performed for a reparameterized version of the logistic random intercept model using spike and slab priors on the parameters subject to selection. Different specifications for these priors are compared on simulated data as well as on a data set where the goal is to identify risk factors for complications after endoscopic retrograde cholangiopancreatography (ERCP).