Abstract
In this article, we review, consolidate and extend a theory for sufficient dimension reduction in regression settings. This theory provides a powerful context for the construction, characterization and interpretation of low-dimensional displays of the data, and allows us to turn graphics into a consistent and theoretically motivated methodological body. In this spirit, we propose an iterative graphical procedure for estimating the meta-parameter which lies at the core of sufficient dimension reduction; namely, the central dimension-reduction subspace.
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Chiaromonte, F., Cook, R.D. Sufficient Dimension Reduction and Graphics in Regression. Annals of the Institute of Statistical Mathematics 54, 768–795 (2002). https://doi.org/10.1023/A:1022411301790
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DOI: https://doi.org/10.1023/A:1022411301790