GARCH models are commonly used for describing, estimating and predicting the dynamics of financial returns. Here, we relax the usual parametric distributional assumptions of GARCH models and develop a Bayesian semiparametric approach based on modeling the innovations using the class of scale...

Most of the Bayesian nonparametric models for non-exchangeable data that are used in applications are based on some extension to the multivariate setting of the Dirichlet process, the best known being MacEachern’s dependent Dirichlet process. A comparison of two recently introduced classes of...

A full Bayesian analysis is developed for an extension to the short-term and long-term hazard ratios model that has been previously introduced. This model is specified by two parameters, short- and long-term hazard ratios respectively, and an unspecified baseline function. Furthermore, the model...

We propose Bayesian inference in hazard regression models where the baseline hazard is unknown, covariate effects are possibly agevarying (non-proportional), and there is multiplicative frailty with arbitrary distribution. Our framework incorporates a wide variety of order restrictions on...

We propose Bayesian inference in hazard regression models where the baseline hazard is unknown, covariate effects are possibly age-varying (non-proportional), and there is multiplicative frailty with arbitrary distribution. Our framework incorporates a wide variety of order restrictions on...

In this paper we estimate income distributions, Lorenz curves and the related Gini index using a Bayesian nonparametric approach based on Polya tree priors. In particular, we propose an alternative approach for dealing with contaminated observations and extreme income values: avoiding the common...