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

Abstract Details

Activity Number: 82
Type: Contributed
Date/Time: Sunday, August 1, 2010 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistics and the Environment
Abstract - #308846
Title: Dimension-Reduced Approach to Modeling of Two Interdependent Space-Time Processes
Author(s): Jenny Brynjarsdottir*+ and L. Mark Berliner
Companies: The Ohio State University and The Ohio State University
Address: 1958 Neil Avenue, Columbus, OH, 43210,
Keywords:
Abstract:

In climatic sciences the objective is often to learn about and predict many interconnected processes in large areas over a long time. For high dimensional spatiotemporal data it is valuable to have techniques that can capture in lower dimensions the most essential structure. Here we develop a dimension reduced approach to modeling and prediction of two space-time processes that may be defined on different spaces. We present a spatially continuous space-time model where one process is modeled conditionally on the other. Assuming that both processes can be expressed in terms of a finite number of spatial basis functions we show how the spatially continuous model can be expressed only in terms of coefficients of these basis function. We also discuss using basis vectors obtained by Canonical Correlation Analysis or Maximum Covariance Analysis for such a model.


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