Abstract #300284

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JSM 2003 Abstract #300284
Activity Number: 243
Type: Contributed
Date/Time: Tuesday, August 5, 2003 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Stat. Sciences
Abstract - #300284
Title: Exploring Latent Structure in Multivariate Spatial Temporal Processes
Author(s): Catherine A. Calder*+
Companies: Duke University
Address: 2738 Quarry Lake Dr., Columbus, OH, 43204-4953,
Keywords: Bayesian ; dynamic factor model ; dimension reduction ; latent process ; atmospheric science
Abstract:

Bayesian process convolution models provide an appealing approach for modeling spatial temporal data. Their structure can be exploited to significantly reduce the dimensionality of a complex spatial temporal process. Dynamic process convolution models can easily be extended to model multivariate spatial time series. Instead of specifying the cross-covariance structure directly, we construct an underlying dynamic factor model that provides insight into the covariance structure. By constructing a factor model, we further reduce the model's dimension temporally. Each of the factors evolves over time and the data are modeled as a smoothed weighted average of these underlying factor processes. Inference procedures remain computationally tractable due to the additional reduction in the dimensionality of the model. We illustrate this model using multivariate pollutant data.

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