A semi-parametric approach to feature selection in high-dimensional linear regression models

Yuyang Liu (), Pengfei Pi () and Shan Luo ()
Additional contact information
Yuyang Liu: Shanghai Jiao Tong University
Pengfei Pi: Shanghai Jiao Tong University
Shan Luo: Shanghai Jiao Tong University

Computational Statistics, 2023, vol. 38, issue 2, No 17, 979-1000

Abstract: Abstract We propose a novel semi-parametric approach to feature selection in high-dimensional linear regression models. This sequential procedure is robust to the unknown error distribution including heavy-tailed distributions. At each step of this procedure, we add the feature with the largest absolute value of the estimated partial profile score into the model. The procedure terminates when a model selection criterion is met. Theoretically, we establish this procedure’s selection consistency under regular conditions. Computationally, extensive numerical studies together with a real data application are provided to demonstrate its advantage over existing representative methods in terms of selection accuracy and computation cost.

Keywords: Semi-parametric; Sequential feature selection; Estimated partial profile score; Score matching; Selection consistency (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00180-022-01254-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:38:y:2023:i:2:d:10.1007_s00180-022-01254-z

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s00180-022-01254-z

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().