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Activity Number:
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187
- Bayesian Analysis of Spatial and Time Series Data
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Type:
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Contributed
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Date/Time:
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Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #312247
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Title:
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Bayesian Nonparametric Density Autoregression with Lag Selection
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Author(s):
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Matthew Heiner* and Athanasios Kottas
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Companies:
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Brigham Young University and University of California, Santa Cruz
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Keywords:
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Dirichlet process mixtures;
Dynamical system;
Local regression;
Markov chain Monte Carlo;
Order selection
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Abstract:
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We develop a Bayesian nonparametric autoregressive model applied to flexibly estimate transition densities exhibiting nonlinear lag dependence. Our approach is related to Bayesian density regression using Dirichlet process mixtures, with the Markovian likelihood defined through the conditional distribution obtained from the mixture. This results in a Bayesian nonparametric extension of a mixtures-of-experts model formulation. We illustrate and explore inferences available through the base model by fitting to synthetic and real time series. We then explore model extensions to include global and local selection among a pre-specified set of lags, and modifications to the kernel weight function to accommodate heterogeneous dynamics. We also compare transition density estimation performance for alternate configurations of the proposed model.
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Authors who are presenting talks have a * after their name.
