Sure Independence Screening for Ultrahigh-Dimensional Additive Model with Multivariate Response

Yongshuai Chen and Baosheng Liang ()
Additional contact information
Yongshuai Chen: School of Statistics, Capital University of Economics and Business, Beijing 100070, China
Baosheng Liang: Department of Biostatistics, School of Public Health, Peking University, Beijing 100191, China

Mathematics, 2025, vol. 13, issue 10, 1-17

Abstract: This paper investigated an ultrahigh-dimensional feature screening approach for additive models with multivariate responses. We proposed a nonparametric screening procedure based on random vector correlations between each predictor and multivariate response, and we established the theoretical results of sure screening and ranking consistency properties under regularity conditions. We also developed an iterative sure independence screening algorithm for convenient and efficient implementation. Extensive finite-sample simulations and a real data example demonstrate the superiority of the proposed procedure over 58–100% of existing candidates. On average, the proposed method outperforms 79% of existing methods across all scenarios considered.

Keywords: sure independence screening; ultrahigh dimensional; additive model; multivariate response; random vector correlation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/10/1558/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/10/1558/ (text/html)

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:gam:jmathe:v:13:y:2025:i:10:p:1558-:d:1652155

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().