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