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Activity Number: 53
Type: Invited
Date/Time: Sunday, July 29, 2012 : 4:00 PM to 5:50 PM
Sponsor: General Methodology
Abstract - #303676
Title: A Simple Class of Bayesian Nonparametric Autoregression Models
Author(s): Fernando Andrés Quintana*+ and Peter Mueller and Maria Anna Di Lucca and Alessandra Guglielmi
Companies: Pontificia Universidad Católica de Chile and The University of Texas at Austin and Università Bocconi and Politecnico de Milano
Address: , Santiago, International, RM22, Chile
Keywords: dependent Dirichlet process ; binary data ; latent variables

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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