for ordinary nonparametric kernel regression and for nonparametric generalized linear model kernel regression constructed estimators with lower order bias than the usual estimators, without the need for devices such as second derivative estimation and multiple bandwidths of different order. We deri
Nonparametric kernel estimation of an isotropic variogram
✍ Scribed by Pilar H. Garcı́a-Soidán; Manuel Febrero-Bande; Wenceslao González-Manteiga
- Publisher
- Elsevier Science
- Year
- 2004
- Tongue
- English
- Weight
- 364 KB
- Volume
- 121
- Category
- Article
- ISSN
- 0378-3758
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✦ Synopsis
In this paper, we propose nonparametric kernel estimators of the semivariogram, under the assumption of isotropy. At ÿrst, a symmetric kernel is considered in order to construct a consistent estimator, so that the selection of the bandwidth parameter is treated via the MSE or the MISE criteria. Next, the use of a boundary kernel will be suggested in order to obtain satisfactory estimates near the semivariogram endpoint. In all cases, an adaptation of Shapiro and Botha's ÿt is proposed to produce valid semivariogram estimators. Finally, we describe a numerical study carried out to illustrate the performance of the kernel estimators.
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