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...

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...

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...

A wide variety of priors have been proposed for nonparametric Bayesian estimation of conditional distributions, and there is a clear need for theorems providing conditions on the prior for large support, as well as posterior consistency. Estimation of an uncountable collection of conditional...

We propose a mixed multinomial logit model, with the mixing distribution assigned a general (nonparametric) stick-breaking prior. We present a Markov chain Monte Carlo (MCMC) algorithm to sample and estimate the posterior distribution of the model’s parameters. The algorithm relies on a Gibbs...

We define a class of reinforced urn processes, based on Hoppe’s urn scheme, that are Markov exchangeable, with a countable and possibly unknown state space. This construction extends the reinforced urn processes developed by Muliere et al. (2000) and widely used in Bayesian nonparametric...

This introduction to Bayesian statistics presents the main concepts as well as the principal reasons advocated in favour of a Bayesian modelling. We cover the various approaches to prior determination as well as the basis asymptotic arguments in favour of using Bayes estimators. The testing...