Standard regression approaches assume that some finite number of the response
distribution characteristics, such as location and scale, change as a (parametric or …

Data analysis sometimes requires the relaxation of parametric assumptions in order to gain
modeling flexibility and robustness against mis-specification of the probability model. In the …

Bayesian nonparametric inference is a relatively young area of research and it has recently
undergone a strong development. Most of its success can be explained by the considerable …

Widely used parametric generalized linear models are, unfortunately, a somewhat limited
class of specifications. Nonparametric aspects are often introduced to enrich this class …

In this article we propose a new framework for Bayesian nonparametric modeling with
continuous covariates. In particular, we allow the nonparametric distribution to depend on …

The Dirichlet process is a stochastic proces used in Bayesian nonparametric models of data,
particularly in Dirichlet process mixture models (also known as infinite mixture models). It is …

We consider dependent nonparametric models for related random probability distributions.
For example, the random distributions might be indexed by a categorical covariate indicating …

Many models for the study of point-referenced data explicitly introduce spatial random
effects to capture residual spatial association. These spatial effects are customarily modelled …

In longitudinal random effects models, the random effects are typically assumed to have a
normal distribution in both Bayesian and classical models. We provide a Bayesian model …

In multicenter studies, subjects in different centers may have different outcome distributions.
This article is motivated by the problem of nonparametric modeling of these distributions …