JSM 2004 - Toronto

Abstract #301338

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Activity Number: 373
Type: Topic Contributed
Date/Time: Wednesday, August 11, 2004 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #301338
Title: Discrete Convolutions for Space-time Data
Author(s): Bruno Sanso*+ and Alexandra M. Schmidt
Companies: University of California, Santa Cruz and Universidade Federal de Rio de Janeiro
Address: School of Engineering, Santa Cruz, CA, 95064,
Keywords: spatio-temporal models ; Bayesian statistics ; process convolutions
Abstract:

We consider a class of models for spatio-temporal processes based on convolving independent processes with a discrete kernel that is represented by a lower triangular matrix. We consider two separate families: one obtained by convolving spatial Gaussian processes with isotropic correlations and using the kernel to provide temporal dependencies. A second one is based on considering convolutions of AR(p) processes and using the kernel to provide spatial interactions. We find that the proposed families of models provide a rich variety of covariance structures. These include covariance functions that are stationary and separable in space and time as well as time dependent nonseparable and nonisotropic ones.

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