Fisher Lecture: Dimension Reduction in Regression

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