Spring 2001 Seminar Series - February 9, 2001
University of Minnesota
School of Statistics
College of Liberal Arts
Bayesian time-varying autoregressions: Models and Applications
Raquel Prado
Universidad Simon Bolivar
(Statistics Search Candidate)
February 9, 2001
4:00 PM,
110
Ford Hall
Minneapolis, East Bank Campus
Social at 3:30 PM,
300
Ford Hall
Abstract
Time-varying autoregressive (TVAR) models have proven useful in describing
the behavior of long time series that experience changes in quasi-periodic
content over time. Beginning with the class of TVAR models in Bayesian
dynamic linear modeling framework, we review methodological aspects of
time-domain decompositions that provide inferences on the structure
underlying non-stationary time series. Recent model extensions that deal
with model order uncertainty, and are enabled using Markov Chain Monte
Carlo simulation methods, are discussed. We emphasize the relevance of
TVAR modeling in the context of biomedical signal processing, in
particular, we consider analyses of multiple electroencephalographic (EEG)
traces that arise in various neurophysiological settings. We conclude
with comments about current research on multivariate autoregressive models.