Using adaptively weighted large margin classifiers for robust sufficient dimension reduction

Artemiou, Andreas ORCID: https://orcid.org/0000-0002-7501-4090 2019. Using adaptively weighted large margin classifiers for robust sufficient dimension reduction. Statistics 53 (5) , pp. 1037-1051. 10.1080/02331888.2019.1636050

Preview

PDF - Accepted Post-Print Version Download (374kB) | Preview

Abstract

In this paper we combine adaptively weighted large margin classifiers with Support Vector Machine (SVM)-based dimension reduction methods to create dimension reduction methods robust to the presence of extreme outliers. We discuss estimation and asymptotic properties of the algorithm. The good performance of the new algorithm is demonstrated through simulations and real data analysis.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Advanced Research Computing @ Cardiff (ARCCA) Mathematics
Subjects:	Q Science > QA Mathematics
Publisher:	Taylor & Francis
ISSN:	0233-1888
Date of First Compliant Deposit:	20 June 2019
Date of Acceptance:	19 June 2019
Last Modified:	28 Nov 2024 20:45
URI:	https://orca.cardiff.ac.uk/id/eprint/123584

Actions (repository staff only)

Edit Item