Abstract #302011

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JSM 2003 Abstract #302011
Activity Number: 368
Type: Contributed
Date/Time: Wednesday, August 6, 2003 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract - #302011
Title: Time Series Modeling via Hierarchical Mixtures
Author(s): Gabriel Huerta*+ and Wenxin Jiang and Martin A. Tanner
Companies: University of New Mexico and Northwestern University and Northwestern University
Address: Dept. of Mathematics and Statistics, Albuquerque, NM, 87131-1141,
Keywords: time series ; Mixture-of-Experts ; cCovariates ; MCMC methods
Abstract:

We address the problem of model comparison and model mixing in time series using the approach known as Hierarchical Mixture-of-Experts. Our methodology allows for comparisons of arbitrary models, not restricted to a particular class or parametric form. Additionally, the approach is flexible enough to incorporate exogenous information that can be summarized in terms of covariables or simply time, through weighting functions that define the hierarchical mixture. We show how to estimate the parameters of such models using the EM-algorithm and with Markov chain Monte Carlo simulation. Finally, we explore our methodology by analyzing a section of the U.S. Industrial Production Index.

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