One-dimensional Nonstationary Process Variance Function Estimation

Many spatial processes exhibit nonstationary features. We estimate a variance function from a single process observation where the errors are nonstationary and correlated. We propose a difference-based approach for a one-dimensional nonstationary process and develop a bandwidth selection method for...

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Hauptverfasser:	Kim, Eunice J, Zhu, Zhengyuan
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Schlagworte:	Statistics - Methodology
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creator	Kim, Eunice J Zhu, Zhengyuan
description	Many spatial processes exhibit nonstationary features. We estimate a variance function from a single process observation where the errors are nonstationary and correlated. We propose a difference-based approach for a one-dimensional nonstationary process and develop a bandwidth selection method for smoothing, taking into account the correlation in the errors. The estimation results are compared to that of a local-likelihood approach proposed by Anderes and Stein(2011). A simulation study shows that our method has a smaller integrated MSE, easily fixes the boundary bias problem, and requires far less computing time than the likelihood-based method.
doi_str_mv	10.48550/arxiv.1605.06579
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title	One-dimensional Nonstationary Process Variance Function Estimation
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