R. Cook

Publications265
h-index62
Citations19,875
Highly Influential Citations1,757
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Assessment of Local Influence
SUMMARY Statistical models usually involve some degree of approximation and therefore are nearly always wrong. Because of this inexactness, an assessment of the influence of minor perturbations of…
Detection of Influential Observation in Linear Regression
  • R. Cook
  • Mathematics
    Technometrics
  • 1 February 2000
A new measure based on confidence ellipsoids is developed for judging the contribution of each data point to the determination of the least squares estimate of the parameter vector in full rank…
Residuals and Influence in Regression
Residuals and Influence in Regression.
Diagnostics for heteroscedasticity in regression
TLDR
The purpose here is to provide appropriate diagnostic techniques to aid in an assessment of the validity of the usual assumption of homoscedasticity when little or no replication is present.
Fisher Lecture: Dimension Reduction in Regression
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…
On the Interpretation of Regression Plots
Abstract A framework is developed for the interpretation of regression plots, including plots of the response against selected covariates, residual plots, added-variable plots, and detrended…
Regression Graphics
This article, which is based on an Interface tutorial, presents an overview of regression graphics, along with an annotated bibliography. The intent is to discuss basic ideas and issues without…
Comments on "Sliced inverse regression for dimension reduction" by K. C. Li
Dimension reduction for conditional mean in regression
TLDR
The notion of the Central Mean Subspace (CMS), a natural inferential object for dimension reduction when the mean function is of interest, is introduced and methods to estimate it are developed.
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