Inverse regression for ridge recovery II: Numerics
Numerical Analysis
2018-08-10 v2
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.
Keywords
Cite
@article{arxiv.1802.01541,
title = {Inverse regression for ridge recovery II: Numerics},
author = {Andrew Glaws and Paul G. Constantine and R. Dennis Cook},
journal= {arXiv preprint arXiv:1802.01541},
year = {2018}
}
Comments
The content has been combined with arXiv:1702.02227