Variable selection method based on BIC with consistency for non-zero partial correlations under a large-dimensional setting

Takayuki Yamada (), Tetsuro Sakurai () and Yasunori Fujikoshi ()
Additional contact information
Takayuki Yamada: Kyoto Women’s University
Tetsuro Sakurai: Suwa University of Science
Yasunori Fujikoshi: Hiroshima University

Computational Statistics, 2025, vol. 40, issue 7, No 9, 3585-3611

Abstract: Abstract This paper addresses the problem of selecting non-zero partial correlations under the assumption of normality. It is cumbersome to compute variable selection criteria for all subsets of variable pairs when the number of variables is large, even if it is smaller than the sample size. To tackle this problem, we propose a fast and consistent variable selection method based on Bayesian information criterion (BIC). The consistency of the method is provided in a high-dimensional asymptotic framework such that the sample size and the number of variables both tend toward infinity under a certain rule. Through numerical simulations, it is shown that the proposed method has a high probability of selecting the true subset of pairs of non-zero partial correlation.

Keywords: Bayesian information criteria; High-dimensional consistency; Partial correlations; KOO approach; Variable selection problem (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00180-025-01628-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:40:y:2025:i:7:d:10.1007_s00180-025-01628-z

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

DOI: 10.1007/s00180-025-01628-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 ().