Abstract Details
Activity Number:
|
143
|
Type:
|
Invited
|
Date/Time:
|
Monday, August 4, 2014 : 10:30 AM to 12:20 PM
|
Sponsor:
|
Section on Bayesian Statistical Science
|
Abstract #314120
|
View Presentation
|
Title:
|
Bayesian Robust Analysis for Large Vector Autoregression Model
|
Author(s):
|
Hongxia Yang*+ and Ban Kawas
|
Companies:
|
IBM Research and IBM Research
|
Keywords:
|
|
Abstract:
|
Vector autoregression (VAR) models have been widely used in economics and econometrics to analyze and predict the linear interdependencies among multiple time series. The number of parameters in VAR models is typically very large rendering the investigation of dynamic relationships computationally challenging. Even the widely used Markov chain Monte Carlo (MCMC) techniques are not able to provide a practical solution for such large-scale general model. We propose a Bayesian robust framework for large VAR models via projection techniques while accommodating uncertainty and observational noise. The resulting model accelerates model evaluations and facilitates efficient MCMC sampling in the reduced parameter space. Practical performance relative to competitors is illustrated in simulations and real data applications.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2014 program
|
2014 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Professional Development program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.