Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/7948
Title: Penalized single-index quantile regression
Authors: Yu, K
Keywords: Dimension reduction;Variable selection;Adaptive lasso;Lasso;Single index model;Quantile regression
Issue Date: 2013
Publisher: Canadian Center of Science and Education
Citation: International Journal of Statistics and Probability, 2(3), 12 - 30, 2013
Abstract: The single-index (SI) regression and single-index quantile (SIQ) estimation methods product linear combinations of all the original predictors. However, it is possible that there are many unimportant predictors within the original predictors. Thus, the precision of parameter estimation as well as the accuracy of prediction will be effected by the existence of those unimportant predictors when the previous methods are used. In this article, an extension of the SIQ method of Wu et al. (2010) has been proposed, which considers Lasso and Adaptive Lasso for estimation and variable selection. Computational algorithms have been developed in order to calculate the penalized SIQ estimates. A simulation study and a real data application have been used to assess the performance of the methods under consideration.
Description: This article is made available through the Brunel Open Access Publishing Fund. Copyright for this article is retained by the author(s), with first publication rights granted to the journal. This is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
URI: http://www.ccsenet.org/journal/index.php/ijsp/article/view/25490
http://bura.brunel.ac.uk/handle/2438/7948
DOI: http://dx.doi.org/10.5539/ijsp.v2n3p12
ISSN: 1927-7032
Appears in Collections:Publications
Brunel OA Publishing Fund
Dept of Mathematics Research Papers
Mathematical Sciences

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