DocumentCode
711873
Title
Quantile Regression Based on Laplacian Manifold Regularization
Author
Ying Zhang
Author_Institution
Econ. & Manage. Sch., Wuhan Univ., Wuhan, China
fYear
2015
fDate
24-26 April 2015
Firstpage
388
Lastpage
394
Abstract
In this paper we present a nonparametric version of a quantile estimator based on Laplacian manifold regularization, which can be obtained by solving a simple quadratic programming problem, and provide bounds on the quantile property and uniform convergence statements of our estimator. Experimental results show the competitiveness of our method with existing ones.
Keywords
Laplace equations; convergence; quadratic programming; regression analysis; Laplacian manifold regularization; nonparametric version; quadratic programming problem; quantile estimator; quantile property; quantile regression; uniform convergence statements; Algorithm design and analysis; Convergence; Estimation; Laplace equations; Manifolds; Optimization; Semisupervised learning; Laplacian Manifold Regularization; Nonlinear Quantile Regression; Semi-supervised Learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Science and Control Engineering (ICISCE), 2015 2nd International Conference on
Conference_Location
Shanghai
Print_ISBN
978-1-4673-6849-0
Type
conf
DOI
10.1109/ICISCE.2015.92
Filename
7120632
Link To Document