The reference prior algorithm (Berger and Bernardo, 1992) is applied to locationscale models with any regular sampling density. A number of two-sample problems is analyzed in this general context, extending the dierence, ratio and product of Normal means problems outside Normality, while...

In this work, we consider modeling the past volatilities through an asymmetric generalised autoregressive conditional heteroskedasticity (Garch) model with heavy tailed sampling distributions. In particular, we consider the Student-t model with unknown degrees of freedom and indicate how it may...

The goal of this document is to present a methodology for estimating probabilities for ordered sets. This may have several practical applications such as calibration of Rating Models, estimation of Mortality Tables or measurement of side effects related to different doze sizes. In order to do...

In this study we evaluate the forecast performance of model averaged forecasts based on the predictive likelihood carrying out a prior sensitivity analysis regarding Zellner’s g prior. The main results are fourfold: First the predictive likelihood does always better than the traditionally...