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February 2007 Fisher Lecture: Dimension Reduction in Regression
R. Dennis Cook
Statist. Sci. 22(1): 1-26 (February 2007). DOI: 10.1214/088342306000000682

Abstract

Beginning with a discussion of R. A. Fisher’s early written remarks that relate to dimension reduction, this article revisits principal components as a reductive method in regression, develops several model-based extensions and ends with descriptions of general approaches to model-based and model-free dimension reduction in regression. It is argued that the role for principal components and related methodology may be broader than previously seen and that the common practice of conditioning on observed values of the predictors may unnecessarily limit the choice of regression methodology.

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R. Dennis Cook. "Fisher Lecture: Dimension Reduction in Regression." Statist. Sci. 22 (1) 1 - 26, February 2007. https://doi.org/10.1214/088342306000000682

Information

Published: February 2007
First available in Project Euclid: 1 August 2007

zbMATH: 1246.62149
MathSciNet: MR2408655
Digital Object Identifier: 10.1214/088342306000000682

Rights: Copyright © 2007 Institute of Mathematical Statistics

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Vol.22 • No. 1 • February 2007
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