Estimation and Prediction of a Class of Convolution-Based Spatial Nonstationary Models for Large Spatial Data

@article{Zhu2010EstimationAP,
  title={Estimation and Prediction of a Class of Convolution-Based Spatial Nonstationary Models for Large Spatial Data},
  author={Zhengyuan Zhu and Yichao Wu},
  journal={Journal of Computational and Graphical Statistics},
  year={2010},
  volume={19},
  pages={74 - 95}
}
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… Expand
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