Robust composite weighted quantile screening for ultrahigh dimensional discriminant analysis

Fengli Song, Peng Lai () and Baohua Shen
Additional contact information
Fengli Song: Nanjing University of Information Science and Technology
Peng Lai: Nanjing University of Information Science and Technology
Baohua Shen: Nanjing University of Information Science and Technology

Metrika: International Journal for Theoretical and Applied Statistics, 2020, vol. 83, issue 7, No 3, 799-820

Abstract: Abstract This paper is concerned with feature screening for the ultrahigh dimensional discriminant analysis. A new feature screening procedure based on the conditional quantile is proposed. The proposed procedure has some desirable features. First, it is model-free which does not require specific discriminant model and can be directly applied to the multi-categories situation. Second, it is robust against heavy-tailed distributions, potential outliers and the sample shortage for some categories, which are very common for high dimensional data. We establish the sure screening property and ranking consistency property of the proposed procedure under some regular conditions. Simulation studies and a real data example are used to assess its finite sample performance.

Keywords: Ultrahigh dimensional discriminant analysis; Feature screening; Robust composite weighted quantile; Sure screening property; Ranking consistency property (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00184-019-00758-x 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:metrik:v:83:y:2020:i:7:d:10.1007_s00184-019-00758-x

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/184/PS2

DOI: 10.1007/s00184-019-00758-x

Access Statistics for this article

Metrika: International Journal for Theoretical and Applied Statistics is currently edited by U. Kamps and Norbert Henze

More articles in Metrika: International Journal for Theoretical and Applied Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().