A Simple Quantile Regression via Support Vector Machine

This paper deals with the estimation of the linear and the nonlinear quantile regressions using the idea of support vector machine. Accordingly, the optimization problem is transformed into the Lagrangian dual problem, which is easier to solve. In particular, for the nonlinear quantile regression th...

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Bibliographic Details
Published inAdvances in Natural Computation pp. 512 - 520
Main Authors Hwang, Changha, Shim, Jooyong
Format Book Chapter Conference Proceeding
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2005
Springer
SeriesLecture Notes in Computer Science
Subjects
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Exact sciences and technology
Statistical analysis
Lagrangian
Non linear regression
Vector support machine
Regression analysis
Linear estimation
Quantile
Kernel method
Mathematical programming
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Summary:This paper deals with the estimation of the linear and the nonlinear quantile regressions using the idea of support vector machine. Accordingly, the optimization problem is transformed into the Lagrangian dual problem, which is easier to solve. In particular, for the nonlinear quantile regression the idea of kernel function is introduced, which allows us to perform operations in the input space rather than the high dimensional feature space. Experimental results are then presented which illustrate the performance of the proposed method.
ISBN:3540283234
9783540283232
ISSN:0302-9743
1611-3349
DOI:10.1007/11539087_66