Variance Estimation and Kriging Prediction for a Class of Non-Stationary Spatial Models
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.
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.