We represent random variables $Z$ that take values in $\Re^n-\{0\}$ as $Z=RY$, where $R$ is a positive random variable and $Y$ takes values in an $(n-1)$-dimensional space $\cal Y$. By fixing the distribution of either $R$ or $Y$, while imposing independence between them, different classes of...

We formulate a general representation of points z E Rn - {O} in terms of pairs (y, r), where r 0, y lies in some space y, and z = ry. In addition, we impose that the representation is unique. An example of such a representation is polar coordinates. As an immediate consequence, we can represent...

This paper considers a Bayesian analysis of the linear regression model under independent sampling from general scale mixtures of Normals. Using a common reference prior, we investigate the validity of Bayesian inference and the existence of posterior moments of the regression and scale...

We investigate the issue of model uncertainty in cross-country growth regressions using Bayesian model averaging (BMA). We find that the posterior probability is very spread among many models suggesting the superiority of BMA over choosing any single model. Out-of-sample predictive results...

We model daily catches of fishing boats in the Grand Bank fishing grounds. We use data on catches per species for a number of vessels collected by the European Union in the context of the Northwest Atlantic Fisheries Organization. Many variables can be thought to influence the amount caught: a...

We investigate the issue of model uncertainty in cross-country growth regressions using Bayesian Model Averaging (BMA). We find that the posterior probability is very spread among many models suggesting the superiority of BMA over choosing any single model. Out-of-sample predictive results...

This paper has two main purposes. Firstly, we develop various ways of defining efficiency in the case of multiple output production. We specifically consider the case where some of the outputs are undesirable, such as pollutants. We investigate how these efficiency definitions relate to one...

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 specification of the prior. In particular, 'diffuse' priors on model-specific parameters can lead to quite unexpected...

The reference prior algorithm (Berger and Bernardo 1992) is applied to multivariate location-scale models with any regular sampling density, where we establish the irrelevance of the usual assumption of Normal sampling if our interest is in either the location or the scale. This result...