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Restricted support vector quantile regression without crossing  

Shim, Joo-Yong (Department of Applied Statistics, Catholic University of Daegu)
Lee, Jang-Taek (Department of Statistics, Dankook University)
Publication Information
Journal of the Korean Data and Information Science Society / v.21, no.6, 2010 , pp. 1319-1325 More about this Journal
Abstract
Quantile regression provides a more complete statistical analysis of the stochastic relationships among random variables. Sometimes quantile functions estimated at different orders can cross each other. We propose a new non-crossing quantile regression method applying support vector median regression to restricted regression quantile, restricted support vector quantile regression. The proposed method provides a satisfying solution to estimating non-crossing quantile functions when multiple quantiles for high dimensional data are needed. We also present the model selection method that employs cross validation techniques for choosing the parameters which aect the performance of the proposed method. One real example and a simulated example are provided to show the usefulness of the proposed method.
Keywords
Cross validation technique; location-scale model; quantile regression; restricted regression quantile; support vector quantile regression;
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Times Cited By KSCI : 4  (Citation Analysis)
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