In contrast to conventional model selection criteria, the Focused Information Criterion (FIC) allows for purpose-specifi c choice of models. This accommodates the idea that one kind of model might be highly appropriate for inferences on a particular parameter, but not for another. Ever since its...

In contrast to conventional model selection criteria, the Focused Information Criterion (FIC) allows for the purpose-specific choice of model specifications. This accommodates the idea that one kind of model might be highly appropriate for inferences on a particular focus parameter, but not for...

In contrast to conventional model selection criteria, the Focused Information Criterion (FIC) allows for purpose-specific choice of models. This accommodates the idea that one kind of model might be highly appropriate for inferences on a particular parameter, but not for another. Ever since its...

In contrast to conventional model selection criteria, the Focused Information Criterion (FIC) allows for the purpose-specific choice of model specifications. This accommodates the idea that one kind of model might be highly appropriate for inferences on a particular focus parameter, but not for...

This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the...

This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the...