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A Simple Class of Bayesian Nonparametric Autoregression Models
Maria Anna Di Lucca1, Alessandra Guglielmi2, Peter Müller3
1Karolinska Institutet, Stockholm, SWEDEN maria.di.lucca@ki.se.
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.
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