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http://dx.doi.org/10.7465/jkdi.2017.28.1.227

Geographically weighted least squares-support vector machine  

Hwang, Changha (Department of Applied Statistics, Dankook University)
Shim, Jooyong (Department of Statistics, Inje University)
Publication Information
Journal of the Korean Data and Information Science Society / v.28, no.1, 2017 , pp. 227-235 More about this Journal
Abstract
When the spatial information of each location is given specifically as coordinates it is popular to use the geographically weighted regression to incorporate the spatial information by assuming that the regression parameters vary spatially across locations. In this paper, we relax the linearity assumption of geographically weighted regression and propose a geographically weighted least squares-support vector machine for estimating geographically weighted mean by using the basic concept of kernel machines. Generalized cross validation function is induced for the model selection. Numerical studies with real datasets have been conducted to compare the performance of proposed method with other methods for predicting geographically weighted mean.
Keywords
Generalized cross validation function; geographically weighted regression; kernel machine; least squares-support vector machine; model selection; spatial information;
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Times Cited By KSCI : 2  (Citation Analysis)
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