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

Abstract Details

Activity Number: 179
Type: Contributed
Date/Time: Monday, August 2, 2010 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #309225
Title: Multivariate Spatial Factor Analysis Using Gaussian Predictive Processes
Author(s): Qian Ren*+ and Sudipto Banerjee
Companies: University of Minnesota and University of Minnesota
Address: 13800 Chestnut Drive, Eden Prairie, MN, 55344,
Keywords: Bayesian factor analysis ; Gaussian predictive process ; Latent variables ; Markov Chain Monte Carlo ; Multivariate spatial analysis

Multivariate spatial data often arise in the natural and environmental sciences, where inferential needs require joint modeling of several correlated outcome variables. Hierarchical factor analysis models posit that a smaller set of latent variables can capture multivariate dependencies, thereby reducing the model dimension. In the spatial context, dimension reduction is also required with respect to the number of observed locations. Here, we demonstrate how a dimension-reducing low-rank spatial process (called a predictive process) leads to a class of computationally feasible spatial factor analysis model, thereby reducing the computational burden. Various additional modeling and implementation issues will be discussed and we will illustrate our methodology with simulated data as well as an environmental data set.

The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2010 program

2010 JSM Online Program Home

For information, contact or phone (888) 231-3473.

If you have questions about the Continuing Education program, please contact the Education Department.