RIS ID
34287
Abstract
The Mat´ern correlation function provides great flexibility for modeling spatially correlated random processes in two dimensions, in particular via a smoothness parameter, whose estimation allows data to determine the degree of smoothness of a spatial process. The extension to include anisotropy provides a very general and flexible class of spatial covariance functions that can be used in a model-based approach to geostatistics, in which parameter estimation is achieved via REML and prediction is within the E-BLUP framework. In this article we develop a general class of linear mixed models using an anisotropic Mat´ern class with an extended metric. The approach is illustrated by application to soil salinity data in a rice-growing field in Australia, and to fine-scale soil pH data. It is found that anisotropy is an important aspect of both datasets, emphasizing the value of a straightforward and accessible approach to modeling anisotropy.
Publication Details
Haskard, K. A., Cullis, B. R. & Verbyla, A. P. (2007). Anisotropic matern correlation and spatial prediction using REML. Journal of Agricultural, Biological, and Environmental Statistics, 12 (2), 147-160.