Estimation of covariance parameters in kriging via restricted maximum likelihood |
| |
| Authors: | C R Dietrich and M R Osborne |
| |
| Institution: | (1) Centre for Resource and Environmental Studies, Australian National University, G.P.O. Box 4, 2601 Canberra, A.C.T.;(2) Centre for Mathematical Analysis, Australian National University, G.P.O. Box 4, 2601 Canberra, A.C.T.;(3) Statistics Research Section, School of Mathematical Science, Australian National University, G.P.O. Box 4, 2601 Canberra, A.C.T. |
| |
| Abstract: | In kriging, parametric approaches to covariance (or variogram) estimation require that unknown parameters be inferred from a single realization of the underlying random field. An approach to such an estimation problem is to assume the field to be Gaussian and iteratively minimize a (restricted) negative loglikelihood over the parameter space. In doing so, the associated computational burden can be considerable. Also, it is usually not easy to check whether or not the minimum achieved is global. In this note, we show that in many practical cases, the structure of the covariance (or variogram) function can be exploited so that iterative minimizing algorithms may be advantageously replaced by a procedure that requires the computation of the roots of a simple rational function and the search for the minimum of a function depending on one variable only. As a consequence, our approach allows one to observe in a straightforward fashion the presence of local minima. Furthermore, it is shown that insensitivity of the likelihood function to changes in parameter value can be easily detected. The note concludes with numerical simulations that illustrate some key features of our estimation procedure. |
| |
| Keywords: | Gaussian random field covariance estimation geostatistics eigenvalue decomposition |
| 本文献已被 SpringerLink 等数据库收录！ |
|
