Sufficient dimension reduction based on distance-weighted discrimination

Randall, Hayley, Artemiou, Andreas ORCID: https://orcid.org/0000-0002-7501-4090 and Qiao, Xingye 2021. Sufficient dimension reduction based on distance-weighted discrimination. Scandinavian Journal of Statistics 48 (4) , pp. 1186-1211. 10.1111/sjos.12484

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Abstract

In this paper we introduce a sufficient dimension reduction (SDR) algorithm based on Distance Weighted Discrimination (DWD). Our methods is shown to be robust on the dimension p of the predictors in our problem, and it also utilizes some new computational results in the DWD literature to propose a computationally faster algorithm than the previous classification-based algorithms in the SDR literature. In addition to the theoretical results of similar methods we prove the consistency of our estimate for divergent number of p. Finally, we demonstrate the advantages of our algorithm using simulated and real datasets.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Advanced Research Computing @ Cardiff (ARCCA) Mathematics
Subjects:	Q Science > QA Mathematics
Additional Information:	This is an open access article under the terms of the Creative Commons Attribution License
Publisher:	Wiley
ISSN:	0303-6898
Funders:	EPSRC
Date of First Compliant Deposit:	16 July 2020
Date of Acceptance:	13 July 2020
Last Modified:	01 Aug 2024 10:10
URI:	https://orca.cardiff.ac.uk/id/eprint/133416

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