NDLI: Non-crossing quantile regressions by SVM

Content Provider	IEEE Xplore Digital Library
Author	Takeuchi, I. Furuhashi, T.
Copyright Year	2004
Description	Author affiliation: Dept. of Information Eng., Mie Univ., Tsu, Japan (Takeuchi, I.)
Abstract	Most regression studies focus on the conditional mean estimation. A more informative description of the conditional distribution can be obtained through the conditional quantile estimation. We study on nonparametric conditional quantile estimator using support vector (SV) regression approach. We show that a slight modification of Vapnik's /spl epsi/-insensitive SV regression leads to a nonparametric conditional quantile estimator with L/sub 2/ regularization. With the great flexibility in nonparametric approach, it is quite possible that two or more estimated conditional quantile functions at different orders could cross or overlap each other. This embarrassing phenomenon is called quantile crossing and it has long been one of the challenging problems in the literature. We address the quantile-crossing problem using SV regression approach. With the common use of kernel trick, we derive a non-crossing conditional quantile estimator in the form of a constrained maximization of a piecewise quadratic function. We also propose its efficient Plait's SMO like implementation by exploiting the specific property of the problem.
Starting Page	401
Ending Page	406
File Size	403949
Page Count	6
File Format	PDF
ISBN	0780383591
ISSN	10987576
DOI	10.1109/IJCNN.2004.1379939
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-07-25
Publisher Place	Hungary
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Kernel Tiles Minimization methods Linear programming Constraint optimization
Content Type	Text
Resource Type	Article

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