Graphics for regressions with a binary response

By R. Dennis Cook. 1996

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Citations in the corpus

Supervised Dimensionality Reduction via Distance Correlation Maximization (2016, background): “Under mild conditions the intersection of all such dimension reducing subspaces is itself a dimension reducing subspace, and is called the central subspace (Cook, 1996).”
Sequential sufficient dimension reduction for large p, small n problems (2015, methodology): “Under mild conditions (Cook, 1996; Yin et al., 2008), the CS is well defined and unique.”
Using the Bootstrap to Select One of a New Class of Dimension Reduction Methods (2003, methodology): “…can be assured by placing fairly weak restrictions on aspects of the joint distribution of © 2003 American Statistical Association Journal of the American Statistical Association December 2003, Vol. 98, No. 464, Theory and Methods DOI 10.1198/016214503000000927 968 y and x (Cook 1994a, 1996).”
Fourier Methods for Estimating the Central Subspace and the Central Mean Subspace in Regression (2006, methodology): “The theory of sufficient dimension reduction was originated from the seminal works by Li (1991) and Cook and Weisberg (1991). During the past decade, much progress has been achieved in SDR; see Cook (1998) for a comprehensive account.”
Successive direction extraction for estimating the central subspace in a multiple-index regression (2008, background): “|X denote the intersection of all DRS’s.”
Kernel Sliced Inverse Regression with Applications to Classification (2008, background): “Cook (1994, 1996) named this reduced subspace the central dimension reduction subspace, S Y | X , for the regression of y on x in which { βββ 1 , . . . , βββ B } form a basis for S Y | X .”
Identifying Regression Outliers and Mixtures Graphically (2000, background): “With a binary response there are only two possible slices, which means that SIR can find at most 1D structure (Cook 1996).”
On consistency and sparsity for sliced inverse regression in high dimensions (2015, methodology): “Under mild conditions (Cook [1996]), the intersection of all the EDR spaces is again an EDR space, which is denoted as S and called the central space. Many algorithms were proposed to find such subspace S under the assumption d = dimS ≪ p. This line of research is commonly known as sufficient dimens”
On Directional Regression for Dimension Reduction (2007, unknown): “…reduction for regression, as pioneered by such authors as Li and Duan (1989), Duan and Li (1991), Li (1991, 1992), Cook and Weisberg (1991), and Cook (1994, 1996), is aimed at reducing the dimension of a vector-valued predictor X, while preserving its regression relation with a real-valued…”
The sliced inverse regression algorithm as a maximum likelihood procedure (2009, background): “Additional existence results can be found in Cook (1994, 1996, 1998).”
Testing predictor contributions in sufficient dimension reduction (2004, background): “When the intersection of all subspaces satisfying (1) also satisfies (1) it is called the central subspace (CS) [Cook (1994, 1996, 1998a)] and is denoted by SY |X.”
Shrinkage inverse regression estimation for model‐free variable selection (2009, methodology): “|X uniquely exists under mild conditions (Cook, 1996), and we assume its existence throughout this paper.”
Foundations for Envelope Models and Methods (2015, background): “This is well-known as the linearity condition in the sufficient dimension reduction literature where there denotes a basis for the central subspace (Cook 1996).”
On a Projective Resampling Method for Dimension Reduction With Multivariate Responses (2008, unknown): “See, for example, Li (1991, 1992), Cook and Weisberg (1991), and Cook (1994, 1996, 1998a).”