Bayesian Analysis

A Simple Class of Bayesian Nonparametric Autoregression Models

Maria Anna Di Lucca, Alessandra Guglielmi, Peter Müller, and Fernando A. Quintana

Full-text: Open access

Abstract

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.

Article information

Source
Bayesian Anal., Volume 8, Number 1 (2013), 63-88.

Dates
First available in Project Euclid: 4 March 2013

Permanent link to this document
https://projecteuclid.org/euclid.ba/1362406652

Digital Object Identifier
doi:10.1214/13-BA803

Mathematical Reviews number (MathSciNet)
MR3036254

Zentralblatt MATH identifier
1329.62376

Keywords
binary data dependent Dirichlet process hierarchical Bayesian model latent variables longitudinal data

Citation

Di Lucca, Maria Anna; Guglielmi, Alessandra; Müller, Peter; Quintana, Fernando A. A Simple Class of Bayesian Nonparametric Autoregression Models. Bayesian Anal. 8 (2013), no. 1, 63--88. doi:10.1214/13-BA803. https://projecteuclid.org/euclid.ba/1362406652


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