Graph informed sufficient dimension reduction

Eugen Pircalabelu and Andreas Artemiou

No 2020007, LIDAM Discussion Papers ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)

Abstract: We develop in this manuscript a new method for performing 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 and (ii) D the number of projection vectors can be larger than H − 1, where H is the number of slices. We develop the methodology for the case of sliced inverse regression model and sliced average variance estimation, but extensions to other dimension reduction techniques are straightforward. Theoretical properties of the methodology are developed for the case without a restriction on the relationship between n and p and computational advantages are demonstrated by simulated and real data experiments.

Keywords: SDR; sliced inverse regression; sliced average variance estimation; penalized estimation (search for similar items in EconPapers)
Pages: 18
Date: 2020-01-01
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Persistent link: https://EconPapers.repec.org/RePEc:aiz:louvad:2020007

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