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Estimation and Prediction of a Class of Convolution-Based Spatial Nonstationary Models for Large Spatial Data
| Content Provider | Semantic Scholar |
|---|---|
| Author | Zhu, Zhengyuan Wu, Yichao |
| Copyright Year | 2010 |
| 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 Matern 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 w. |
| Starting Page | 74 |
| Ending Page | 95 |
| Page Count | 22 |
| File Format | PDF HTM / HTML |
| DOI | 10.1198/jcgs.2009.07123 |
| Volume Number | 19 |
| Alternate Webpage(s) | https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1134&context=stat_las_pubs |
| Alternate Webpage(s) | http://stat-or.unc.edu/files/2016/04/07_13.pdf |
| Alternate Webpage(s) | http://stat-or.unc.edu/research/Current%20Reports/techpdf/zhu.wu.2007.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
