title: A Simple Class of Bayesian Nonparametric Autoregression Models.
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.
authors.affiliation: Karolinska Institutet, Stockholm, SWEDEN maria.di.lucca@ki.se.
authors.forename: Maria Anna
authors.initials: MA
authors.lastname: Di Lucca
authors.affiliation: Politecnico di Milano, Milano, ITALY alessandra.guglielmi@polimi.it.
authors.forename: Alessandra
authors.initials: A
authors.lastname: Guglielmi
authors.affiliation: The University of Texas, Austin (TX), USA pmueller@utexas.edu.
authors.forename: Peter
authors.initials: P
authors.lastname: Müller
authors.affiliation: Pontificia Universidad Católica de Chile, Santiago, CHILE quintana@mat.puc.cl.
authors.forename: Fernando A
authors.initials: FA
authors.lastname: Quintana
keywords: binary data; dependent Dirichlet process; hierarchical Bayesian model; latent variables; longitudinal data