One-dimensional Nonstationary Process Variance Function Estimation

Eunice J. Kim; Zhengyuan Zhu

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arXiv · 1605.06579

One-dimensional Nonstationary Process Variance Function Estimation

Eunice J. Kim·Zhengyuan Zhu

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Abstract

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.

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Eunice J. Kim, Zhengyuan Zhu. 2016-05-21. One-dimensional Nonstationary Process Variance Function Estimation. https://arxiv.org/abs/1605.06579

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