Campus Units

Statistics

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

2010

Journal or Book Title

Journal of Computational and Graphical Statistics

Volume

19

Issue

1

First Page

74

Last Page

95

DOI

10.1198/jcgs.2009.07123

Abstract

In this article we address two important issues common to the analysis of large spatial datasets. One is the modeling of nonstationarity, and the other is the computational challenges in doing likelihood-based estimation and kriging prediction. We model the spatial process as a convolution of independent Gaussian processes, with the spatially varying kernel function given by the modified Bessel functions. This is a generalization of the process-convolution approach of Higdon, Swall, and Kern (1999), who used the Gaussian kernel to obtain a closed-form nonstationary covariance function. Our model can produce processes with richer local behavior similar to the processes with the Matérn class of covariance functions. Because the covariance function of our model does not have a closed-form expression, direct estimation and spatial prediction using kriging is infeasible for large datasets. Efficient algorithms for parameter estimation and spatial prediction are proposed and implemented. We compare our method with methods based on stationary model and moving window kriging. Simulation results and application to a rainfall dataset show that our method has better prediction performance. Supplemental materials for the article are available online.

Comments

This is an Accepted Manuscript of an article published by Taylor & Francis as Zhu, Zhengyuan, and Yichao Wu. "Estimation and prediction of a class of convolution-based spatial nonstationary models for large spatial data." Journal of Computational and Graphical Statistics 19, no. 1 (2010): 74-95. Available online DOI: 10.1198/jcgs.2009.07123. Posted with permission.

Copyright Owner

Taylor & Francis

Language

en

File Format

application/pdf

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