Title	Quantile regression in Variable Screening
Creator	Kong, Linglong
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2014-02-12
Description	We introduce a quantile regression framework for linear and nonlinear variable screening with high-dimensional heterogeneous data. Motivated by success of various variable screening methods, especially the quantile-adaptive framework, we propose to combine the information from different quantile levels to provide more efficient variable screening procedure. In particular, there are two ways to do so: one is to simply take (weighted) average across different levels of quantile regression; the other one is to use (weighted) composite quantile regression. Asymptotically, these two approaches are equivalent in terms of efficiency. Numerical studies confirm the fine performance of the proposed method for various linear and nonlinear models. Joint work with Qian Shi.
Extent	35 minutes
Subject	Mathematics; Statistics; Biology and other natural sciences; Applied statistics
Type	Moving Image
File Format	video/mp4
Language	eng
Notes	Author affiliation: University of Alberta
Series	BIRS Workshop Lecture Videos (Banff, Alta)
Date Available	2014-08-07
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivs 2.5 Canada
DOI	10.14288/1.0043886
URI	http://hdl.handle.net/2429/49839
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Faculty
Rights URI	http://creativecommons.org/licenses/by-nc-nd/2.5/ca/
Aggregated Source Repository	DSpace

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Quantile regression in Variable Screening Kong, Linglong

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