Model Selection With Mixed Variables on the Lasso Path

X. Jessie Jeng (), Huimin Peng and Wenbin Lu
Additional contact information
X. Jessie Jeng: North Carolina State University
Huimin Peng: North Carolina State University
Wenbin Lu: North Carolina State University

Sankhya B: The Indian Journal of Statistics, 2021, vol. 83, issue 1, No 8, 170-184

Abstract: Abstract Among the most popular model selection procedures in high-dimensional regression, Lasso provides a solution path to rank the variables and determines a cut-off position on the path to select variables and estimate coefficients. In this paper, we consider variable selection from a new perspective motivated by the frequently occurred phenomenon that relevant variables are often mixed with noise variables on the solution path. We propose to characterize the positions of the first noise variable and the last relevant variable on the path. We then develop a new variable selection procedure to control over-selection of the noise variables ranking after the last relevant variable, and, at the same time, retain a high proportion of relevant variables ranking before the first noise variable. Our procedure utilizes the recently developed covariance test statistic and Q statistic in post-selection inference. In numerical examples, our method compares favorably with existing methods in selection accuracy and the ability to interpret its results.

Keywords: Jayanta K. Ghosh; Large p small n; Penalized regression; Variable selection; Primary: 62F07; Secondary: 62F12 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13571-019-00219-5 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:sankhb:v:83:y:2021:i:1:d:10.1007_s13571-019-00219-5

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/13571

DOI: 10.1007/s13571-019-00219-5

Access Statistics for this article

Sankhya B: The Indian Journal of Statistics is currently edited by Dipak Dey

More articles in Sankhya B: The Indian Journal of Statistics from Springer, Indian Statistical Institute
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().