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Abstract Details
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Activity Number:
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40
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Type:
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Contributed
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Date/Time:
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Sunday, July 29, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #305341 |
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Title:
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Fully Nonparametric Regression for Bounded Data Using Dependent Bernstein Polynomials
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Author(s):
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Alejandro Jara*+ and Andrés Felipe Barrientos and Fernando Andrés Quintana
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Companies:
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Pontificia Universidad Católica de Chile and Pontificia Universidad Católica de Chile and Pontificia Universidad Católica de Chile
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Address:
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, , , Chile
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Keywords:
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Random Bernstein polynomials ;
Dependent processes ;
Dirichlet process ;
Dependent Dirichlet processes ;
Bayesian nonparametrics
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Abstract:
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We propose a novel class of probability models for sets of predictor-dependent probability distributions with bounded domain. The proposal corresponds to an extension of the Dirichlet-Bernstein prior by using dependent stick-breaking processes. Appealing theoretical properties such as full support, continuity, marginal distribution, correlation structure, and consistency of the posterior distribution are studied. Practicable special cases of the general model are discussed and illustrated using simulated data. The simulated data is also used to compare the proposed methodology to existing methods. A. Jara's work is supported by FONDECYT 11100144 grant. F.A. Quintana's work is supported by FONDECYT 1100010 grant.
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