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Activity Number:
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61
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Type:
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Topic Contributed
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Date/Time:
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Sunday, August 2, 2009 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #303417 |
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Title:
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A Generalized Convolution Model for Multivariate Nonstationary Spatial Processes
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Author(s):
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Anandamayee Majumdar*+ and Debashis Paul and Dianne Bautista
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Companies:
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Arizona State University and University of California, Davis and The Ohio State University
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Address:
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Dept. of Math and Statistics, Tempe, AZ, 85287-1804,
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Keywords:
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convolution ; non-stationary process ; posterior inference ; predictive distribution ; spatial
statistics ; spectral density
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Abstract:
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We propose a flexible class of non-stationary stochastic models for multivariate spatial data. The method is based on convolutions of spatially varying covariance kernels and produces mathematically valid covariance structures. This method generalizes the convolution approach suggested by Majumdar and Gelfand (2007) to extend multivariate spatial covariance functions to the nonstationary case. A Bayesian method for estimation of the parameters in the covariance model based on a Gibbs sampler is proposed, and applied to simulated data. Model comparison is performed with the coregionalization model of Wackernagel (2003) which uses a stationary bivariate model. Based on posterior prediction results, the performance of our model is seen to be considerably better.
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