Journal article
A lower bound based smoothed quasi-Newton algorithm for group bridge penalized regression
Communications in statistics. Simulation and computation, Vol.46(6), pp.4694-4707
07/03/2017
DOI: 10.1080/03610918.2015.1129409
Abstract
In this paper, we propose a lower bound based smoothed quasi-Newton algorithm for computing the solution paths of the group bridge estimator in linear regression models. Our method is based on the quasi-Newton algorithm with a smoothed group bridge penalty in combination with a novel data-driven thresholding rule for the regression coefficients. This rule is derived based on a necessary KKT condition of the group bridge optimization problem. It is easy to implement and can be used to eliminate groups with zero coefficients. Thus, it reduces the dimension of the optimization problem. The proposed algorithm removes the restriction of groupwise orthogonal condition needed in coordinate descent and LARS algorithms for group variable selection. Numerical results show that the proposed algorithm outperforms the coordinate descent based algorithms in both efficiency and accuracy.
Details
- Title: Subtitle
- A lower bound based smoothed quasi-Newton algorithm for group bridge penalized regression
- Creators
- Yongxiu Cao - Zhongnan University of Economics and LawJian Huang - Shanghai University of Finance and EconomicsYuling Jiao - Zhongnan University of Economics and LawYanyan Liu - Wuhan University
- Resource Type
- Journal article
- Publication Details
- Communications in statistics. Simulation and computation, Vol.46(6), pp.4694-4707
- Publisher
- Taylor & Francis
- DOI
- 10.1080/03610918.2015.1129409
- ISSN
- 0361-0918
- eISSN
- 1532-4141
- Language
- English
- Date published
- 07/03/2017
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984257747802771
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