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A novel fully automatic Bayesian procedure for variable selection in normal regression model is proposed. The procedure uses the posterior probabilities of the models to drive a stochastic search. The posterior probabilities are computed using intrinsic priors, which can be considered default priors for model selection problems. That is, they are derived(More)
A condition needed for testing nested hypotheses from a Bayesian viewpoint is that the prior for the alternative model concentrates mass around the smaller, or null, model. For testing independence in contingency tables, the intrinsic priors satisfy this requirement. Further, the degree of concentration of the priors is controlled by a discrete parameter m,(More)
It has long been known that for the comparison of pairwise nested models, a decision based on the Bayes factor produces a consistent model selector (in the frequentist sense). Here we go beyond the 1 usual consistency for nested pairwise models, and show that for a wide class of prior distributions, including intrinsic priors, the corresponding Bayesian(More)
In the objective Bayesian approach to variable selection in regression a crucial point is the encompassing of the underlying nonnested linear models. Once the models have been encompassed one can define objective priors for the multiple testing problem involved in the variable selection problem. There are two natural ways of encompassing: one way is to(More)
Clustering is an important and challenging statistical problem for which there is an extensive literature. Modeling approaches include mixture models and product partition models. Here we develop a product partition model and a model selection procedure based on Bayes factors from intrinsic priors. We also find that the choice of the prior on model space is(More)
This paper presents a Bayesian model for meta-analysis of sparse discrete binomial data, which are out of the scope of the usual hierarchical normal random-effect models. Treatment effectiveness data are often of this type. The crucial linking distribution between the effectiveness conditional on the healthcare center and the unconditional effectiveness is(More)
Statistical meta-analysis is mostly carried out with the help of the random effect normal model, including the case of discrete random variables. We argue that the normal approximation is not always able to adequately capture the underlying uncertainty of the original discrete data. Furthermore, when we examine the influence of the prior distributions(More)
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