Campus Units

Statistics

Document Type

Article

Publication Version

Published Version

Publication Date

1-2015

Journal or Book Title

Statistica Sinica

Volume

25

Issue

1

First Page

135

Last Page

149

DOI

10.5705/ss.2013.205w

Abstract

This paper discusses the estimation and plug-in kriging prediction non-stationary spatial process assuming a smoothly varying variance an additive independent measurement error. A difference-based kernel estimator of the variance function and a modified likelihood estimator of the mea surement error variance are used for parameter estimation. Asymptotic properties of these estimators and the plug-in kriging predictor are established. A simula tion study is presented to test our estimation-prediction procedure. Our kriging predictor is shown to perform better than the spatial adaptive local polynomial regression estimator proposed by Fan and Gijbels (1995) when the measurement error is small.

Comments

This article is published as Shu Yang and Zhengyuan Zhu, "Variance Estimation and Kriging Prediction for a Class of Non-stationary Spatial Models," Statistica Sinica 25(1), (2015): 135-149. DOI: 10.5705/ss.2013.205w. Posted with permission.

Copyright Owner

Institute of Statistical Science, Academia Sinica

Language

en

File Format

application/pdf

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