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March 2013 A Simple Class of Bayesian Nonparametric Autoregression Models
Maria Anna Di Lucca, Alessandra Guglielmi, Peter Müller, Fernando A. Quintana
Bayesian Anal. 8(1): 63-88 (March 2013). DOI: 10.1214/13-BA803


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 a dependent Dirichlet process 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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Maria Anna Di Lucca. Alessandra Guglielmi. Peter Müller. Fernando A. Quintana. "A Simple Class of Bayesian Nonparametric Autoregression Models." Bayesian Anal. 8 (1) 63 - 88, March 2013.


Published: March 2013
First available in Project Euclid: 4 March 2013

zbMATH: 1329.62376
MathSciNet: MR3036254
Digital Object Identifier: 10.1214/13-BA803

Keywords: Binary data , dependent Dirichlet process , hierarchical Bayesian model , latent variables , longitudinal data

Rights: Copyright © 2013 International Society for Bayesian Analysis


Vol.8 • No. 1 • March 2013
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