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    A Simple Class of Bayesian Nonparametric Autoregression Models

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    2013_DiLuca.pdf (3.662Mb)
    Date
    2013
    Author
    Di Lucca, Maria Anna
    Guglielmi, Alessandra
    Mueller, Peter
    Quintana, Fernando A.
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    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.
    Department
    Mathematics
    Subject
    binary data
    dependent dirichlet process
    hierarchical bayesian model
    latent variables
    longitudinal data
    stick-breaking processes
    dependent dirichlet processes
    binary
    mixtures
    priors
    mathematics, interdisciplinary applications
    statistics & probability
    URI
    http://hdl.handle.net/2152/43321
    Citation
    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.
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