We first analyse the general problem of admissible conditioning and next consider the evaluation of the loss of information when a non-admissible conditioning is used as an approximation of the exact posterior distribution. Considering the case of Fisher test, we evaluate from a Bayesian point...

Five different identification problems in mixture models are made explicit. Necessary and sufficient relationships among these problems of identification are analyzed using the concepts of weak and strong identification. This analysis is first particularized under a normality assumption and then...

This note argues that a Bayesian framework is almost inescapable when specifying statistical models of the LISREL type, i.e. models involving not only latent and manifest variables but also incidental parameters. Indeed, a careful specification, making every hypothesis explicit and interpretable...

In this paper, we develop a Bayesian analysis of a semi-parametric binary choice model. The prior specification of the functional parameter, namely the distribution function of a latent variable, is of the Dirichlet process type and the prior specification of the Euclidean parameter, namely the...

Five different identification problems in mixture models are made explicit. Necessary and sufficient relationships among these problems of identification are analyzed using the concepts of weak and strong identification. This analysis is first particularized under a normality assumption and then...

In this paper, minimal conditions under which a semi-parametric binary response model is identified in a Bayesian framework are presented and compared to the conditions usually required in a sampling theory framework.