Bi-level feature selection in high dimensional AFT models with applications to a genomic study

Huang Hailin, Shangguan Jizi, Ruan Peifeng and Liang Hua ()
Additional contact information
Huang Hailin: Department of Statistics, George Washington University, Washington, DC 20052, USA
Shangguan Jizi: Department of Statistics, George Washington University, Washington, DC 20052, USA
Ruan Peifeng: Department of Statistics, George Washington University, Washington, DC 20052, USA
Liang Hua: Department of Statistics, George Washington University, Washington, DC 20052, USA

Statistical Applications in Genetics and Molecular Biology, 2019, vol. 18, issue 5, 11

Abstract: We propose a new bi-level feature selection method for high dimensional accelerated failure time models by formulating the models to a single index model. The method yields sparse solutions at both the group and individual feature levels along with an expedient algorithm, which is computationally efficient and easily implemented. We analyze a genomic dataset for an illustration, and present a simulation study to show the finite sample performance of the proposed method.

Keywords: accelerated failure time (AFT) models; group selection; individual feature selection; single-index models (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/sagmb-2019-0016 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

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:bpj:sagmbi:v:18:y:2019:i:5:p:11:n:4

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/sagmb/html

DOI: 10.1515/sagmb-2019-0016

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().