Abstract Details
Activity Number:
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535
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Type:
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Contributed
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Date/Time:
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Wednesday, August 7, 2013 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics and the Environment
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Abstract - #309437 |
Title:
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Nonstationary Process Variance Estimation
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Author(s):
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Eunice Kim*+ and Zhengyuan Zhu
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Companies:
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Iowa State University and Iowa State University
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Keywords:
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Difference-based ;
Variance estimation ;
Correlated errors ;
Nonstationary random field
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Abstract:
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Many spatial processes exhibit nonstationary features. We estimate a variance function from a single process observation where the errors have changing variance and correlated. We assume that the mean process is smooth and that the error process is a product of a smooth standard deviation function and a second-order stationary process. We propose a difference-based approach for a one-dimensional nonstationary process. We also develop a bandwidth selection method for smoothing which takes into account the error dependence strucutre. The estimation results are compared to that of a local-likelihood approach proposed by Anderes and Stein(2011). Simulation study shows that our method has a smaller integrated MSE, fixes the boundary bias problem, and requires far less computing time as the evaluation of likelihood with matrix inversion is not necessary.
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Authors who are presenting talks have a * after their name.
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