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Activity Number:
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558
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Type:
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Contributed
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Date/Time:
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Thursday, August 6, 2009 : 8:30 AM to 10:20 AM
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Sponsor:
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IMS
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| Abstract - #305306 |
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Title:
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On Estimating the Spectral Densities of a Class of Stationary Spatio-Temporal Processes
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Author(s):
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Hui Xu*+ and Herman Rubin
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Companies:
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St. Cloud State University and Purdue University
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Address:
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720 4th Ave S, ECC 250, St. Cloud, MN, 56301,
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Keywords:
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Spectral Densities ; Spatio-temporal Processes ; Bayesian Method ; Robustness ; Autocovariances
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Abstract:
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Estimating the spectral density of a stationary process is equivalent to estimating its autocovariances under integrated squared error loss. With few assumptions made on the specific structure, we propose a data-driven method for estimating the autocovariances of a class of stationary spatio-temporal processes through analyzing the prior Bayes risk. The autocovariance estimators capture the decreasing feature of the true autocovariances, which is important to get the robust results in our spectral density estimation. Large sample properties of the spectral density estimator are discussed and simulation studies are provided.
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