This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 520
Type: Contributed
Date/Time: Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #306550
Title: Bayesian Mixtures of Autoregressive Models
Author(s): Ori Rosn*+ and Sally Wood and Robert Kohn
Companies: The University of Texas at El Paso and Melbourne Business School and University of New South Wales
Address: , , ,
Keywords: Forecasting ; Mixtures-of-experts ; Nonstationary time series ; Reversible jump MCMC
Abstract:

We propose a class of time-domain models for analyzing possibly nonstationary time series. This class of models is formed as a mixture of time series models, whose mixing weights are a function of time. We consider specifically mixtures of autoregressive models with a common but unknown lag. The model parameters, including the number of mixture components, are estimated via Markov chain Monte Carlo methods. The methodology is illustrated with simulated and real data.


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