Graph imposed sliced inverse regression

Pircalabelu, Eugen and Artemiou, Andreas ORCID: https://orcid.org/0000-0002-7501-4090 2021. Graph imposed sliced inverse regression. Computational Statistics & Data Analysis 164 , 107302. 10.1016/j.csda.2021.107302

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Abstract

A new method is developed for performing sufficient dimension reduction when probabilistic graphical models are being used to perform estimation of parameters. The procedure enriches the domain of application of dimension reduction techniques to settings where (i) p the number of variables in the model is much larger than the available sample size n, (ii) p is much larger than the number of slices H the model uses and D the number of projection vectors can be larger than n. The methodology is developed for the case of the sliced inverse regression model, but extensions to other dimension reduction techniques such as sliced average variance estimation or other methods are straightforward.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Advanced Research Computing @ Cardiff (ARCCA) Mathematics
Subjects:	Q Science > QA Mathematics
Publisher:	Elsevier
ISSN:	0167-9473
Date of First Compliant Deposit:	8 June 2021
Date of Acceptance:	4 June 2021
Last Modified:	09 Nov 2024 01:15
URI:	https://orca.cardiff.ac.uk/id/eprint/141758

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