Standard statistical practice ignores model uncertainty. Data analysts typically select a
model from some class of models and then proceed as if the selected model had generated …

The method of model averaging has become an important tool to deal with model
uncertainty, for example in situations where a large amount of different theories exist, as are …

We consider the problem of accounting for model uncertainty in linear regression models.
Conditioning on a single selected model ignores model uncertainty, and thus leads to the …

This paper describes and compares various hierarchical mixture prior formulations of
variable selection uncertainty in normal linear regression models. These include the …

In contrast to a posterior analysis given a particular sampling model, posterior model
probabilities in the context of model uncertainty are typically rather sensitive to the …

The growth of biostatistics has been phenomenal in recent years and has been marked by
considerable technical innovation in both methodology and computational practicality. One …

A simple method for subset selection of independent variables in regression models is
proposed. We expand the usual regression equation to an equation that incorporates all …

We provide a review of prior distributions for objective Bayesian analysis. We start by
examining some foundational issues and then organize our exposition into priors for: i) …

In principle, the Bayesian approach to model selection is straightforward. Prior probability
distributions are used to describe the uncertainty surrounding all unknowns. After observing …

" Since the publication of the first edition, Bayesian statistics is, arguably, still not the norm in
the formal quantitative methods training of social scientists. Typically, the only introduction …