A Simple Class of Bayesian Nonparametric Autoregression Models

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

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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 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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Di Lucca, Maria Anna, Alessandra Guglielmi, Peter Müller, and Fernando A. Quintana. "A simple class of Bayesian nonparametric autoregression models." Bayesian analysis (Online), Vol. 8, No. 1 (2013): 63.