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.
Original language | English (US) |
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Pages (from-to) | 983-992 |
Number of pages | 10 |
Journal | Journal of the American Statistical Association |
Volume | 91 |
Issue number | 435 |
DOIs | |
State | Published - Sep 1 1996 |
Keywords
- Dimension-reduction subspaces
- Graphical regression
- Logistic regression
- Sliced inverse regression