Dimension reduction for the conditional mean in regressions with categorical predictors

By Bing Li, R. Dennis Cook, Francesca Chiaromonte. 2003

93 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/1258272881f46733f2df1ba243cab078d8026da3

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In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Dimension reduction in regression without matrix inversion (2007, methodology): “The approach, which requires computation of powers of ̂ instead of ̂−1, is similar to that of iterative Hessian transformations (Cook & Li, 2002, 2004), and our method encompasses partial least squares as a special case.”
On a Projective Resampling Method for Dimension Reduction With Multivariate Responses (2008, background): “See Cook and Li (2002, 2004).”
Sufficient Dimension Reduction via Inverse Regression (2005, background): “Illustrations on sufficient dimension reduction are available throughout the literature (see, e.g., Chen and Li 1998; Li, Cook, and Chiaromonte 2003).”
Smoothed Functional Inverse Regression (2005, methodology): “Moreover, other methods have been proposed for estimating ED: pHd (Li (1992)), SAVE (Cook (1991)), PIR (Bura and Cook (2001)), MAVE (Xia, Tong, Li and Zhu (2002)), estimation of the Central Mean Subspace (Cook and Li (2002)) and use of the conditional kth moment (Yin and Cook (2002)).”
Contour regression: A general approach to dimension reduction (2005, methodology): “Similar tests have been constructed in other contexts in [15, 16]; [4], Chapter 11 and [2, 5, 14].”
Martingale Difference Correlation and Its Use in High-Dimensional Variable Screening (2014, unknown): “See Section 4.2 for a detailed finite sample comparison between QaSIS of He, Wang, and Hong (2013) and our MDC-SISQ.”
Testing predictor contributions in sufficient dimension reduction (2004, methodology): “Other dimension reduction methods estimate the central mean subspace [Cook and Li (2002)], which is a subspace of the CS that captures the mean function.”
On estimation efficiency of the central mean subspace (2014, background): “For example, in the partial dimension reduction problems (Li et al., 2003), it is assumed that E.Y |x/ = E.Y |βTx1, x2/.”