We introduce a new class of discrete random probability measures that extend the definition of Dirichlet process (DP) by explicitly incorporating skewness. The asymmetry is controlled by a single parameter in such a way ...

The early work of Zellner on the multivariate Student-t linear model has been extended to Bayesian inference for linear models with dependent non-normal error terms, particularly through various papers by Osiewalski, Steel ...

This work considers probability models for partitions of a set of n elements using a predictive approach, i.e., models that are specified in terms of the conditional probability of either joining an already existing cluster ...

Based on a constructive representation, which distinguishes between a skewing mechanism P and an underlying symmetric distribution F, we introduce two flexible classes of distributions. They are generated by nonparametric ...

Flexible regression is a traditional motivation for the development of non-parametric Bayesian models. A popular approach for this involves a joint model for responses and covariates, from which the desired result arises ...

We discuss Bayesian nonparametric procedures for the regression analysis of compositional responses, that is, data supported on a multivariate simplex. The procedures are based on a modified class of multivariate Bernstein ...

The quality of products derived from reforestation woods is directly linked to the density observed in tree trunks. Due to the natural correlation between the densities collected along the longitudinal trunk discs, linear ...