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Applications of sufficient dimension reduction algorithms on non-elliptical data

Artemiou, Andreas 2014. Applications of sufficient dimension reduction algorithms on non-elliptical data. Journal of the Indian Society of Agricultural Statistics 68 (2) , pp. 273-283.

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Abstract

Sufficient dimension reduction (SDR) is a class of supervised dimension reduction techniques which generally perform much better than unsupervised dimension reduction techniques like Principal Component Analysis (PCA). In this paper we present classic methodology in the SDR framework that is based on inverse moments and we discuss the theoretical assumptions. At the end we demonstrate the advantage of a recently introduced method known as Principal Support Vector Machine (PSVM) in the presence of predictors which violate the theoretical assumption of ellipticity of the marginal distribution.

Item Type: Article
Status: Published
Schools: Mathematics
Subjects: Q Science > QA Mathematics
Publisher: Indian Society of Agricultural Statistics
ISSN: 0019-6363
Date of Acceptance: 26 May 2014
Last Modified: 05 Mar 2019 15:54
URI: http://orca.cf.ac.uk/id/eprint/61653

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