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arXiv · 1802.01541

Inverse regression for ridge recovery II: Numerics

Andrew Glaws·Paul G. Constantine·R. Dennis Cook
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Abstract

We investigate the application of sufficient dimension reduction (SDR) to a noiseless data set derived from a deterministic function of several variables. In this context, SDR provides a framework for ridge recovery. In this second part, we explore the numerical subtleties associated with using two inverse regression methods---sliced inverse regression (SIR) and sliced average variance estimation (SAVE)---for ridge recovery. This includes a detailed numerical analysis of the eigenvalues of the resulting matrices and the subspaces spanned by their columns. After this analysis, we demonstrate the methods on several numerical test problems.

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Andrew Glaws, Paul G. Constantine, R. Dennis Cook. 2018-02-05. Inverse regression for ridge recovery II: Numerics. https://arxiv.org/abs/1802.01541

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