Journal article
A group adaptive elastic-net approach for variable selection in high-dimensional linear regression
Science China. Mathematics, Vol.61(1), pp.173-188
01/01/2018
DOI: 10.1007/s11425-016-0071-x
Abstract
In practice, predictors possess grouping structures spontaneously. Incorporation of such useful information can improve statistical modeling and inference. In addition, the high-dimensionality often leads to the collinearity problem. The elastic net is an ideal method which is inclined to reflect a grouping effect. In this paper, we consider the problem of group selection and estimation in the sparse linear regression model in which predictors can be grouped. We investigate a group adaptive elastic-net and derive oracle inequalities and model consistency for the cases where group number is larger than the sample size. Oracle property is addressed for the case of the fixed group number. We revise the locally approximated coordinate descent algorithm to make our computation. Simulation and real data studies indicate that the group adaptive elastic-net is an alternative and competitive method for model selection of high-dimensional problems for the cases of group number being larger than the sample size.
Details
- Title: Subtitle
- A group adaptive elastic-net approach for variable selection in high-dimensional linear regression
- Creators
- Jianhua Hu - Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai 200433, Peoples R ChinaJian Huang - University of IowaFeng Qiu - Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai 200433, Peoples R China
- Resource Type
- Journal article
- Publication Details
- Science China. Mathematics, Vol.61(1), pp.173-188
- Publisher
- SCIENCE PRESS
- DOI
- 10.1007/s11425-016-0071-x
- ISSN
- 1674-7283
- eISSN
- 1869-1862
- Number of pages
- 16
- Grant note
- 201309KF02 / Open Research Fund Program of Key Laboratory of Mathematical Economics (SUFE) IRT13077 / Changjiang Scholars and Innovative Research Team in University 11571219 / National Natural Science Foundation of China Ministry of Education
- Language
- English
- Date published
- 01/01/2018
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984257631402771
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