Journal
MATHEMATICAL GEOLOGY
Volume 32, Issue 3, Pages 249-270Publisher
SPRINGER/PLENUM PUBLISHERS
DOI: 10.1023/A:1007563809463
Keywords
nonparametric; variogram fitting; derivative estimation; generalized least squares; model selection; aliasing
Ask authors/readers for more resources
Before optimal linear prediction can be performed on spatial data sets, the variogram is usually estimated at various lags and a parametric model is fitted to those estimates. Apart from possible a priori knowledge about the process and the user's subjectivity, there is no standard methodology for choosing among valid variogram models like the spherical or the exponential ones. This paper discusses the nonparametric estimation of the variogram and its derivative, based on the spectral representation of positive definite functions. The use of the estimated derivative to help choose among valid parametric variogram models is presented. Once a model is selected, its parameters can be estimated-for example, by generalized least squares. A small simulation study is performed that demonstrates the usefulness of estimating the derivative to help model selection and illustrates the issue of aliasing.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available