Rejoinder: Fisher Lecture: Dimension Reduction in Regression
نویسندگان
چکیده
منابع مشابه
Comment: Fisher Lecture: Dimension Reduction in Regression
This paper puts dimension reduction into the historical context of sufficiency, efficiency and principal component analysis, and opens up an avenue toward efficient dimension reduction via maximum likelihood estimation of inverse regression. I congratulate Professor Cook for this insightful and groundbreaking work. My discussion will focus on two points that explore and extend Cook’s ideas. The...
متن کاملComment: Fisher Lecture: Dimension Reduction in Regression
I am pleased to participate in this well-deserved recognition of Dennis Cook’s remarkable career. Cook points out Fisher’s insistence that predictor variables in regression be chosen without reference to the dependent variable. Reduction by principal components clearly satisfies that dictum. One of my primary objections to partial least squares regression when I first encountered it as an alter...
متن کاملFisher Lecture: Dimension Reduction in Regression1,21,21,21,2
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 rel...
متن کاملFisher Lecture: Dimension Reduction in Regression1,2
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 rel...
متن کاملDimension Reduction Regression in R
Regression is the study of the dependence of a response variable on a collection predictors collected in . In dimension reduction regression, we seek to find a few linear combinations , such that all the information about the regression is contained in these linear combinations. If is very small, perhaps one or two, then the regression problem can be summarized using simple graphics; for exampl...
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ژورنال
عنوان ژورنال: Statistical Science
سال: 2007
ISSN: 0883-4237
DOI: 10.1214/088342307000000078