Journal article
Hybrid estimation of semivariogram parameters
Mathematical geology, Vol.39(2), pp.247-260
02/2007
DOI: 10.1007/s11004-006-9070-8
Abstract
Two widely used methods of semivariogram estimation are weighted least squares stimation and maximum likelihood estimation. The former have certain computational advantages, whereas the latter are more statistically efficient. We introduce and study a "hybrid" semivariogram estimation procedure that combines weighted least squares estimation of the range parameter with maximum likelihood estimation of the sill (and nugget) assuming known range, in such a way that the sill-torange ratio in an exponential semivariogramis estimated consistently under an infill asymptotic regime. We show empirically that such a procedure is nearly as efficient computationally, and more efficient statistically for some parameters, than weighted least squares estimation of all of the semivariogram's parameters. Furthermore, we demonstrate that standard plug-in (or empirical) spatial predictors and prediction error variances, obtained by replacing the unknown semivariogram parameters with estimates in expressions for the ordinary kriging predictor and kriging variance, respectively, perform better when hybrid estimates are plugged in than when weighted least squares estimates are plugged in. In view of these results and the simplicity of computing the hybrid estimates from weighted least squares estimates, we suggest that software that currently estimates the semivariogram by weighted least squares methods be amended to include hybrid estimation as an option. © Springer Science+Business Media, LLC 2007.
Details
- Title: Subtitle
- Hybrid estimation of semivariogram parameters
- Creators
- Hao Zhang - Washington State UniversityDale L. Zimmerman - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Mathematical geology, Vol.39(2), pp.247-260
- Publisher
- Springer
- DOI
- 10.1007/s11004-006-9070-8
- ISSN
- 0882-8121
- eISSN
- 1573-8868
- Language
- English
- Date published
- 02/2007
- Academic Unit
- Biostatistics; Statistics and Actuarial Science
- Record Identifier
- 9984257628102771
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