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.
Copyright Owner
Taylor & Francis
Copyright Date
2010
Language
en
File Format
application/pdf
Recommended Citation
Zhu, Zhengyuan and Wu, Yichao, "Estimation and Prediction of a Class of Convolution-Based Spatial Nonstationary Models for Large Spatial Data" (2010). Statistics Publications. 135.
https://lib.dr.iastate.edu/stat_las_pubs/135
Included in
Design of Experiments and Sample Surveys Commons, Multivariate Analysis Commons, Statistical Models Commons
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.