NDLI: Asymptotic analysis of the Huberized LASSO estimator

Content Provider	IEEE Xplore Digital Library
Author	Xiaohui Chen Wang, Z.J. McKeown, M.J.
Copyright Year	2010
Description	Author affiliation: Department of Electrical and Computer Engineering, University of British Columbia, Canada (Xiaohui Chen; Wang, Z.J.) \|\| Department of Medicine (Neurology), University of British Columbia, Canada (McKeown, M.J.)
Abstract	The Huberized LASSO model, a robust version of the popular LASSO, yields robust model selection in sparse linear regression. Though its superior performance was empirically demonstrated for large variance noise, currently no theoretical asymptotic analysis has been derived for the Huberized LASSO estimator. Here we prove that the Huberized LASSO estimator is consistent and asymptotically normal distributed under a proper shrinkage rate. Our derivation shows that, unlike the LASSO estimator, its asymptotic variance is stabilized in the presence of noise with large variance. We also propose the adaptive Huberized LASSO estimator by allowing unequal penalty weights for the regression coefficients, and prove its model selection consistency. Simulations confirm our theoretical results.
Starting Page	1898
Ending Page	1901
File Size	123206
Page Count	4
File Format	PDF
ISBN	9781424442959
ISSN	15206149
DOI	10.1109/ICASSP.2010.5495338
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-03-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Noise robustness Linear regression Vectors Analysis of variance Predictive models Nervous system Performance analysis Parameter estimation Loss measurement Noise measurement model selection consistency Sparse linear regression Huberized LASSO robustness asymptotic normality
Content Type	Text
Resource Type	Article

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