Extending Sliced Inverse Regression

By Efstathia Bura. 2001

107 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/bb0e3ee01c9d9b7d89358e373b202c6590e9ac49

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. R. Dennis Cook
2. Chenlei Leng
3. Kenji Fukumizu
4. Taiji Suzuki
5. Liqiang Ni
6. Masashi Sugiyama

In Management Science and Operations Research

1. Masashi Sugiyama
2. R. Dennis Cook
3. Taiji Suzuki
4. Sanford Weisberg
5. Yongwu Shao
6. Chenlei Leng
7. Kenji Fukumizu
8. Liqiang Ni

In Statistics and Probability

1. Masashi Sugiyama
2. Taiji Suzuki
3. Chenlei Leng
4. Sanford Weisberg
5. Yongwu Shao
6. R. Dennis Cook
7. Liqiang Ni
8. Kenji Fukumizu

Citations in the corpus

• Marginal tests with sliced average variance estimation (2007, background): “Bura & Cook (2001) established the asymptotic distribution for general regressors.”
• Sufficient Dimension Reduction via Squared-Loss Mutual Information Estimation (2010, background): “LSMI inherits good properties from MLMI and, moreover, provides an analytic SMI estimator that gives an analytic formula for its derivative (see Table 1).”
• Sufficient Dimension Reduction via Inverse Regression (2005, methodology): “…sliced inverse regression (SIR) (Li 1991), sliced average variance estimation (SAVE) (Cook and Weisberg 1991), graphical regression (Cook 1994, 1998a), parametric inverse regression (Bura and Cook 2001a), and partial SIR (Chiaromonte, Cook, and Li 2002) when categorical predictors are present.”
• Using intraslice covariances for improved estimation of the central subspace in regression (2006, methodology): “…estimator (7) with the inverse regression estimators (4) and the slice inverse regression estimator (3) using the weighted chi-squared test (Bura & Cook, 2001) for hypotheses on d. Figures 1(a) and (b) show, for 4 and 8 slices, the percentage in 1000 replications of correct dimension…”
• Gradient-Based Kernel Dimension Reduction for Regression (2014, methodology): “…have been developed for the choice of dimensionality in respective dimension reduction methods (Schott, 1994, Ferr´e, 1998, Cook and Lee, 1999, Bura and Cook, 2001, Yin and Seymour, 2005, Li and Wang, 2007, Li et al., 2011, to list some), they are derived from asymptotic analysis of some…”