Feature screening filter for high dimensional survival data in the reproducing kernel Hilbert space

Sanghun Jeong, Sanghun Shin and Hojin Yang

Communications in Statistics - Theory and Methods, 2025, vol. 54, issue 13, 4101-4120

Abstract: We consider the issue of screening uninformative variables associate with survival outcome in ultra high-dimensional situation. specifically, we focus on Hilbert-Schmits Independent criteria (HSIC) statistic defined in the Reproducing kernel Hilbert Space(RKHS) and use the HSIC as a feature screening approach to avoid from difficulty occurred from the ultra high dimensionality. Generally, it is known that the HSIC can account for the flexible relationship between the outcome and predictor variables. Compared with the typical approaches such as regularization and model free screening approaches, our approach does not require the complex non parametric computation or the complicated numerical optimization, Thereby, our proposed approach effectively and flexibly filters out the uninformative predictor. We will show the advantages of our method in numerical simulation studies and illustrate the usefulness of it by applying to diffuse large-B-cell lymphoma data.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2024.2413846 (text/html)
Access to full text is restricted to subscribers.

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:taf:lstaxx:v:54:y:2025:i:13:p:4101-4120

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2024.2413846

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().