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Abstract Central dimension-reduction subspaces, which characterize the dependence of a response variable on one or more predictors, are developed and then used to guide the construction and interpretation of graphics for regression problems with a binary response variable. Graphical methods requiring neither a link function nor residuals are suggested for both development and criticism of model components implied by the central dimension-reduction subspace. Key Words: Dimension-reduction subspacesGraphical regressionLogistic regressionSliced inverse regression
R. Dennis Cook (Sun,) studied this question.
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