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Abstract Details

Activity Number: 245
Type: Contributed
Date/Time: Monday, August 2, 2010 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #307236
Title: A New Class of Semiparametric Semvariogram and Nugget Estimators
Author(s): Patrick Carmack*+ and Jeffrey Spence and William R. Schucany and Richard Gusnt and Qihua Lin and Robert Haley
Companies: University of Central Arkansas and The University of Texas Southwestern Medical Center at Dallas and Southern Methodist University and Southern Methodist University and The University of Texas Southwestern Medical Center at Dallas and The University of Texas Southwestern Medical Center at Dallas
Address: UCA Box 4912, Conway, AR, 72035, USA
Keywords: nonparametric ; Bessel basis ; isotropic ; node space ; regular lattice ; negative definiteness
Abstract:

Several authors have proposed nonparametric semivariogram estimators. Shapiro & Botha (1991) did so by application of Bochner's theorem and Cherry et al. (1996) further investigated this technique where it performed favorably against parametric estimators even when data were generated under the parametric model. While this approach is sound, it lacks nugget estimation which is essential to spatial modeling and proper statistical inference. We propose a modified form of this method, which admits nugget estimation and broadens the basis. This is achieved by a simple change to the basis and an appropriate restriction of the node space as dictated by the first root of the Bessel function of the first kind of order ?. The efficacy of this new unsupervised method is demonstrated via simulation. We conclude with remarks about selecting the appropriate basis and node space definition.


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