Browsing by Author "Barrientos, Andres F."

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    A Bayesian Nonparametric Multiple Testing Procedure for Comparing Several Treatments Against a Control
    (2019) Gutierrez, Luis; Barrientos, Andres F.; Gonzalez, Jorge; Taylor-Rodriguez, Daniel
    We propose a Bayesian nonparametric strategy to test for differences between a control group and several treatment regimes. Most of the existing tests for this type of comparison are based on the differences between location parameters. In contrast, our approach identifies differences across the entire distribution, avoids strong modeling assumptions over the distributions for each treatment, and accounts for multiple testing through the prior distribution on the space of hypotheses. The proposal is compared to other commonly used hypothesis testing procedures under simulated scenarios. Two real applications are also analyzed with the proposed methodology.
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    On the Support of MacEachern's Dependent Dirichlet Processes and Extensions
    (INT SOC BAYESIAN ANALYSIS, 2012) Barrientos, Andres F.; Jara, Alejandro; Quintana, Fernando A.
    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 definition of MacEachern's dependent Dirichlet processes. Based on this definition, we provide sufficient conditions for the full weak support of different versions of the process. In particular, we show that under mild conditions on the copula functions, the version where only the support points or the weights are dependent on predictors have full weak support. In addition, we also characterize the Hellinger and Kullback-Leibler support of mixtures induced by the different versions of the dependent Dirichlet process. A generalization of the results for the general class of dependent stick-breaking processes is also provided.