Browsing by Author "Quintana, Fernando A."

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    A predictive view of Bayesian clustering
    (ELSEVIER, 2006) Quintana, Fernando A.
    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 or forming a new one. The inherent structure can be motivated by resorting to hierarchical models of either parametric or nonparametric nature. Parametric examples include the product partition models (PPMs) and the model-based approach of Dasgupta and Raftery (J. Amer. Statist. Assoc. 93 (1998) 294), while nonparametric alternatives include the Dirichlet process, and more generally, the species sampling models (SSMs). Under exchangeability, PPMs and SSMs induce the same type of partition structure. The methods are discussed in the context of outlier detection in normal linear regression models and of (univariate) density estimation. (c) 2004 Elsevier B.V. All rights reserved.
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    Flexible Univariate Continuous Distributions
    (INT SOC BAYESIAN ANALYSIS, 2009) Quintana, Fernando A.; Steel, Mark F. J.; Ferreira, Jose T. A. S.
    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 modelling of either P or F. We examine properties of these distributions and consider how they can help us to identify which aspects of the data are badly captured by simple symmetric distributions. Within a Bayesian framework, we investigate useful prior settings and conduct inference through MCMC methods. On the basis of simulated and real data examples, we make recommendations for the use of our models in practice. Our models perform well in the context of density estimation using the multimodal galaxy data and for regression modelling with data on the body mass index of athletes.