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.
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. https://doi.org/10.1214/13-BA803