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Abstract Details
Activity Number:
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53
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Type:
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Invited
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Date/Time:
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Sunday, July 29, 2012 : 4:00 PM to 5:50 PM
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Sponsor:
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General Methodology
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Abstract - #303676 |
Title:
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A Simple Class of Bayesian Nonparametric Autoregression Models
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Author(s):
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Fernando Andrés Quintana*+ and Peter Mueller and Maria Anna Di Lucca and Alessandra Guglielmi
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Companies:
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Pontificia Universidad Católica de Chile and The University of Texas at Austin and Università Bocconi and Politecnico de Milano
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Address:
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, Santiago, International, RM22, Chile
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Keywords:
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dependent Dirichlet process ;
binary data ;
latent variables
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Abstract:
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We introduce a model for a time series of continuous outcomes, that can be expressed as fully nonparametric regression or density regression on lagged terms. The model is based on dependent Dirichlet processes prior on a family of random probability measures indexed by the lagged covariates. The approach is also extended to sequences of binary responses. We discuss implementation and applications of the models to a sequence of waiting times between eruptions of the Old Faithful Geyser, and to a dataset consisting of sequences of recurrence indicators for tumors in the bladder of several patients.
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