This work proposes a method to assess the influence of individual observations in the clustering generated by any process that involves random partitions. We call it Similarity Analysis. It basically consists of decomposing ...

We propose a fully nonparametric modelling approach for time-to-event regression data, when the response of interest can only be determined to lie in an interval obtained from a sequence of examination times and the ...

We consider the problem of combining inference in related nonparametric Bayes models. Analogous to parametric hierarchical models, the hierarchical extension formalizes borrowing strength across the related submodels. In ...

We discuss inference for data with repeated measurements at multiple levels. The motivating example is data with blood counts from cancer patients undergoing multiple cycles of chemotherapy, with days nested within cycles. ...

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 ...

This work addresses the density estimation problem using non-parametric Bayesian approach. It is considered hierarchical mixture models where the uncertainty about the mixing measure is modeled using the Dirichlet process ...

We study the support properties of Dirichlet process-based models for sets of predictor-dependent probability distributions. Exploiting the connection between copulas and stochastic processes, we provide an alternative ...

We propose a Dirichlet process mixture of mixtures of Dirichlet models for density estimation. By assuming random sampling from a density belonging to a Holder class, we show that the posterior distribution of the model ...