FDR control and power analysis for high-dimensional logistic regression via StabKoff

Panxu Yuan, Yinfei Kong and Gaorong Li ()
Additional contact information
Panxu Yuan: Beijing Normal University
Yinfei Kong: California State University
Gaorong Li: Beijing Normal University

Statistical Papers, 2024, vol. 65, issue 5, No 4, 2719-2749

Abstract: Abstract Identifying significant variables for the high-dimensional logistic regression model is a fundamental problem in modern statistics and machine learning. This paper introduces a stability knockoffs (StabKoff) selection procedure by merging stability selection and knockoffs to conduct controlled variable selection for logistic regression. Under some regularity conditions, we show that the proposed method achieves FDR control under the finite-sample setting, and the power also asymptotically approaches one as the sample size tends to infinity. In addition, we further develop an intersection strategy that allows better separation of knockoff statistics between significant and unimportant variables, which in some cases leads to an increase in power. The simulation studies demonstrate that the proposed method possesses satisfactory finite-sample performance compared with existing methods in terms of both FDR and power. We also apply the proposed method to a real data set on opioid use disorder treatment.

Keywords: False discovery rate; Logistic regression; Power analysis; Stability knockoffs; Variable selection; 62J12; 62J07; 62H15 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00362-023-01501-5 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:stpapr:v:65:y:2024:i:5:d:10.1007_s00362-023-01501-5

Ordering information: This journal article can be ordered from
http://www.springer. ... business/journal/362

DOI: 10.1007/s00362-023-01501-5

Access Statistics for this article

Statistical Papers is currently edited by C. Müller, W. Krämer and W.G. Müller

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