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Activity Number: 535
Type: Contributed
Date/Time: Wednesday, August 7, 2013 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract - #309437
Title: Nonstationary Process Variance Estimation
Author(s): Eunice Kim*+ and Zhengyuan Zhu
Companies: Iowa State University and Iowa State University
Keywords: Difference-based ; Variance estimation ; Correlated errors ; Nonstationary random field
Abstract:

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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