Save: a method for dimension reduction and graphics in regression

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On Sliced Inverse Regression With High-Dimensional Covariates (2006, methodology): “(Cook and Weisberg 1991; Cook 2000), parametric inverse regression (PIR) (Bura and Cook 2001a,b), and the proposed hybrid methods of Zhu, Ohtaki, and Li (2006) that are convex combinations of SIR and SAVE.”
Sufficient Dimension Reduction via Squared-Loss Mutual Information Estimation (2010, methodology): “Dimension Reduction for Artificial Datasets: We use6 artificial datasets (3 datasets designed by us and3 datasets borrowed from [12]; see Figure 1), and compare LDR with kernel dimension reduction (KDR) [12], the Hilbert-Schmidt independence criterion (HSIC) [15], sliced inverse regression (SIR) [20”
Sufficient Component Analysis for Supervised Dimension Reduction (2011, methodology): “We use four artiﬁcial datasets, and compare the proposed SCA, LSDR 1 (Suzuki & Sugiyama, 2010), KDR 2 (Fukumizu et al., 2009), sliced inverse regression (SIR) 3 (Li, 1991), sliced average variance estimation (SAVE) 3 (Cook, 2000), and principal Hessian direction (pHd) 3 (Li, 1992).”
Asymptotics for sliced average variance estimation (2007, methodology): “When n = 200, a simulation was conducted with H = 2, 5, 10, 20 and 50, but we only report the results with H = 10 for illustration because for practical use, H = 10 is a good choice for this sample size (see relevant references such as [5, 16, 28]).”