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Asymmetric least squares regression estimation using weighted least squares support vector machine  

Hwan, Chang-Ha (Department of Statistics, Dankook University)
Publication Information
Journal of the Korean Data and Information Science Society / v.22, no.5, 2011 , pp. 999-1005 More about this Journal
Abstract
This paper proposes a weighted least squares support vector machine for asymmetric least squares regression. This method achieves nonlinear prediction power, while making no assumption on the underlying probability distributions. The cross validation function is introduced to choose optimal hyperparameters in the procedure. Experimental results are then presented which indicate the performance of the proposed model.
Keywords
Asymmetric least squares regression; cross validation; expectile; least squares support vector machine; percentile;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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