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Variable Selection and Parameter Estimation with the Atan Regularization Method
Joint Authors
Source
Journal of Probability and Statistics
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-03-16
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Variable selection is fundamental to high-dimensional statistical modeling.
Many variable selection techniques may be implemented by penalized least squares using various penalty functions.
In this paper, an arctangent type penalty which very closely resembles l 0 penalty is proposed; we call it Atan penalty.
The Atan-penalized least squares procedure is shown to consistently select the correct model and is asymptotically normal, provided the number of variables grows slower than the number of observations.
The Atan procedure is efficiently implemented using an iteratively reweighted Lasso algorithm.
Simulation results and data example show that the Atan procedure with BIC-type criterion performs very well in a variety of settings.
American Psychological Association (APA)
Wang, Yanxin& Zhu, Li. 2016. Variable Selection and Parameter Estimation with the Atan Regularization Method. Journal of Probability and Statistics،Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1110264
Modern Language Association (MLA)
Wang, Yanxin& Zhu, Li. Variable Selection and Parameter Estimation with the Atan Regularization Method. Journal of Probability and Statistics No. 2016 (2016), pp.1-12.
https://search.emarefa.net/detail/BIM-1110264
American Medical Association (AMA)
Wang, Yanxin& Zhu, Li. Variable Selection and Parameter Estimation with the Atan Regularization Method. Journal of Probability and Statistics. 2016. Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1110264
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1110264