M. P. Barrios and S. Velilla, A bootstrap method for assessing the dimension of a general regression problem, Statistics & Probability Letters, vol.77, issue.3, pp.247-255, 2007.
DOI : 10.1016/j.spl.2006.07.020

C. Bernard-michel, L. Gardes, and S. Girard, Gaussian Regularized Sliced Inverse Regression, Statistics and Computing, vol.5, issue.22, pp.85-98, 2009.
DOI : 10.1137/1.9780898717570
URL : https://hal.archives-ouvertes.fr/inria-00180458

M. Chavent, S. Girard, V. Kuentz-simonet, B. Liquet, T. M. Nguyen et al., A sliced inverse regression approach for data stream, Computational Statistics, vol.51, issue.22, pp.1129-1152, 2014.
DOI : 10.1093/bioinformatics/bti680
URL : https://hal.archives-ouvertes.fr/hal-00688609

M. Chavent, V. Kuentz, B. Liquet, and J. Saracco, A Sliced Inverse Regression Approach for a Stratified Population, Communications in Statistics - Theory and Methods, vol.5, issue.21, pp.3857-3878, 2011.
DOI : 10.1214/aos/1032526955

C. Chen and K. Li, Can sir be as popular as multiple linear regression?, Statistica Sinica, vol.8, issue.2, pp.289-316, 1998.

F. Chiaromonte and J. Martinelli, Dimension reduction strategies for analyzing global gene expression data with a response, Mathematical Biosciences, vol.176, issue.1, pp.123-144, 2002.
DOI : 10.1016/S0025-5564(01)00106-7

R. D. Cook and S. Weisberg, Applied regression including computing and graphics, 2009.
DOI : 10.1002/9780470316948

R. Coudret, S. Girard, and J. Saracco, A new sliced inverse regression method for multivariate response, Computational Statistics & Data Analysis, vol.77, pp.285-299, 2014.
DOI : 10.1016/j.csda.2014.03.006
URL : https://hal.archives-ouvertes.fr/hal-00714981

P. Diaconis and D. Freedman, Asymptotics of Graphical Projection Pursuit, The Annals of Statistics, vol.12, issue.3, pp.793-815, 1984.
DOI : 10.1214/aos/1176346703

N. Duan and K. Li, Slicing Regression: A Link-Free Regression Method, The Annals of Statistics, vol.19, issue.2, pp.505-530, 1991.
DOI : 10.1214/aos/1176348109

K. Fukunaga, Introduction to statistical pattern recognition, 2013.

P. Hall and K. Li, On almost Linearity of Low Dimensional Projections from High Dimensional Data, The Annals of Statistics, vol.21, issue.2, pp.867-889, 1993.
DOI : 10.1214/aos/1176349155

T. Hsing and R. J. Carroll, An Asymptotic Theory for Sliced Inverse Regression, The Annals of Statistics, vol.20, issue.2, pp.1040-1061, 1992.
DOI : 10.1214/aos/1176348669

V. Kuentz and J. Saracco, Cluster-based Sliced Inverse Regression, Journal of the Korean Statistical Society, vol.39, issue.2, pp.251-267, 2010.
DOI : 10.1016/j.jkss.2009.08.004
URL : https://hal.archives-ouvertes.fr/hal-00547252

K. Li, Sliced Inverse Regression for Dimension Reduction, Journal of the American Statistical Association, vol.13, issue.414, pp.316-327, 1991.
DOI : 10.1214/aos/1176345514

L. Li, R. D. Cook, and C. J. Nachtsheim, Cluster-based estimation for sufficient dimension reduction, Computational Statistics & Data Analysis, vol.47, issue.1, pp.175-193, 2004.
DOI : 10.1016/j.csda.2003.10.017

L. Li and H. Li, Dimension reduction methods for microarrays with application to censored survival data, Bioinformatics, vol.20, issue.18, pp.3406-3412, 2004.
DOI : 10.1093/bioinformatics/bth415

B. Liquet and J. Saracco, A graphical tool for selecting the number of slices and the dimension of the model in SIR and SAVE approaches, Computational Statistics, vol.98, issue.1, pp.103-125, 2012.
DOI : 10.1016/j.jmva.2006.11.005
URL : https://hal.archives-ouvertes.fr/hal-00938090

J. Saracco, An asymptotic theory for sliced inverse regression, Communications in Statistics - Theory and Methods, vol.5, issue.9, pp.2141-2171, 1997.
DOI : 10.1214/aos/1176345514

L. Scrucca, Regularized Sliced Inverse Regression with Applications in Classification, pp.59-66, 2006.
DOI : 10.1007/3-540-35978-8_7

L. Scrucca, Class prediction and gene selection for DNA microarrays using regularized sliced inverse regression, Computational Statistics & Data Analysis, vol.52, issue.1, pp.438-451, 2007.
DOI : 10.1016/j.csda.2007.02.005

M. Soltanolkotabi, E. Elhamifar, and E. J. Candes, Robust subspace clustering, The Annals of Statistics, vol.42, issue.2, pp.669-699, 2014.
DOI : 10.1214/13-AOS1199SUPP

L. J. Van-der-maaten, E. O. Postma, and H. J. Van-den-herik, Dimensionality reduction: A comparative review, Journal of Machine Learning Research, vol.10, pp.1-41, 2009.

W. Zhong, P. Zeng, P. Ma, J. S. Liu, and Y. Zhu, RSIR: regularized sliced inverse regression for motif discovery, Bioinformatics, vol.21, issue.22, pp.4169-4175, 2005.
DOI : 10.1093/bioinformatics/bti680

L. Zhu, Extending the Scope of Inverse Regression Methods in Sufficient Dimension Reduction, Communications in Statistics - Theory and Methods, vol.5, issue.1, pp.84-95, 2010.
DOI : 10.1093/biomet/asq018

]. R. References and . Cook, Graphics for regressions with a binary response, Journal of the American Statistical Association, vol.91, issue.1435, pp.983-992, 1996.

X. Yin, B. Li, and R. D. Cook, Successive direction extraction for estimating the central subspace in a multiple-index regression, Journal of Multivariate Analysis, vol.99, issue.8, pp.1733-1757, 2008.
DOI : 10.1016/j.jmva.2008.01.006

K. Li, Sliced Inverse Regression for Dimension Reduction, Journal of the American Statistical Association, vol.13, issue.414, pp.316-327, 1991.
DOI : 10.1214/aos/1176345514

P. Hall and K. Li, On almost Linearity of Low Dimensional Projections from High Dimensional Data, The Annals of Statistics, vol.21, issue.2, pp.867-889, 1993.
DOI : 10.1214/aos/1176349155

K. Fukumizu, F. R. Bach, and M. I. Jordan, Dimensionality reduction for supervised learning with reproducing kernel Hilbert spaces, The Journal of Machine Learning Research, vol.5, pp.73-99, 2004.
DOI : 10.21236/ADA446572

B. Li and Y. Dong, Dimension reduction for nonelliptically distributed predictors, The Annals of Statistics, vol.37, issue.3, pp.1272-1298, 2009.
DOI : 10.1214/08-AOS598

K. Fukumizu, F. R. Bach, and M. I. Jordan, Kernel dimension reduction in regression, The Annals of Statistics, vol.37, issue.4, pp.1871-1905, 2009.
DOI : 10.1214/08-AOS637

R. D. Cook and S. Weisberg, Sliced Inverse Regression for Dimension Reduction: Comment, Journal of the American Statistical Association, vol.86, issue.414, pp.328-332, 1991.
DOI : 10.2307/2290564

R. D. Cook, Fisher Lecture: Dimension Reduction in Regression, Statistical Science, vol.22, issue.1, pp.1-26, 2007.
DOI : 10.1214/088342306000000682

E. Bura and R. D. Cook, Extending Sliced Inverse Regression, Journal of the American Statistical Association, vol.96, issue.455, pp.996-1003, 2001.
DOI : 10.1198/016214501753208979

S. J. Sheather and J. W. Mckean, A comparison of procedures based on inverse regression, Lecture Notes-Monograph Series, vol.31, pp.271-278, 1997.
DOI : 10.1214/lnms/1215454143

U. Gather, T. Hilker, and C. Becker, A note On outlier sensitivity of Sliced Inverse Regression, Statistics, vol.27, issue.4, pp.271-281, 2002.
DOI : 10.1214/aos/1032526955

U. Gather, T. Hilker, and C. Becker, A Robustified Version of Sliced Inverse Regression, Statistics in Genetics and in the Environmental Sciences, pp.147-157, 2001.
DOI : 10.1007/978-3-0348-8326-9_10

Y. Dong, Z. Yu, and L. Zhu, Robust inverse regression for dimension reduction, Journal of Multivariate Analysis, vol.134, pp.71-81, 2015.
DOI : 10.1016/j.jmva.2014.10.005

G. Mclachlan and D. Peel, Robust mixture modelling using the t distribution, Statistics and Computing, vol.10, pp.339-348, 2000.

D. F. Andrews and C. L. Mallows, Scale mixtures of normal distributions, Journal of the Royal Statistical Society. Series BMethodological), vol.36, issue.1, pp.99-102, 1974.

L. Ferré, Determining the dimension in sliced inverse regression and related methods, Journal of the American Statistical Association, vol.93, issue.441, pp.132-140, 1998.

B. Liquet and J. Saracco, A graphical tool for selecting the number of slices and the dimension of the model in SIR and SAVE approaches, Computational Statistics, vol.98, issue.1, pp.2012-103
DOI : 10.1016/j.jmva.2006.11.005
URL : https://hal.archives-ouvertes.fr/hal-00938090

J. R. Schott, Determining the Dimensionality in Sliced Inverse Regression, Journal of the American Statistical Association, vol.16, issue.425, pp.425-141, 1994.
DOI : 10.1214/aos/1176345514

S. Velilla, Assessing the Number of Linear Components in a General Regression Problem, Journal of the American Statistical Association, vol.5, issue.443, pp.1088-1098, 1998.
DOI : 10.1214/aos/1176343954

L. Zhu, B. Miao, and H. Peng, On Sliced Inverse Regression With High-Dimensional Covariates, Journal of the American Statistical Association, vol.101, issue.474, pp.630-643, 2006.
DOI : 10.1198/016214505000001285

R. E. Kass and A. E. Raftery, Bayes Factors, Journal of the American Statistical Association, vol.2, issue.430, pp.773-795, 1995.
DOI : 10.1214/ss/1177013241

C. Giraud, Introduction to high-dimensional statistics, Chapman and Hall, CRC Monographs on Statistics and Applied Probability, 2014.

H. Wang, L. Ni, and C. Tsai, Improving dimension reduction via contourprojection, Statistica Sinica, vol.18, issue.1, pp.299-311, 2008.

R. Luo, H. Wang, and C. Tsai, Contour projected dimension reduction, The Annals of Statistics, vol.37, issue.6B, pp.3743-3778, 2009.
DOI : 10.1214/08-AOS679

J. Zhou, Robust dimension reduction based on canonical correlation, Journal of Multivariate Analysis, vol.100, issue.1, pp.195-209, 2009.
DOI : 10.1016/j.jmva.2008.04.003

R. D. Cook, L. Forzani, and D. R. Tomassi, : A Package for Likelihood-Based Sufficient Dimension Reduction, Journal of Statistical Software, vol.39, issue.3, pp.1-20, 2011.
DOI : 10.18637/jss.v039.i03

A. Chiancone, S. Girard, and J. Chanussot, Collaborative sliced inverse regression , Communications in Statistics -Theory and Methods

L. Barreda, A. Gannoun, and J. Saracco, Some extensions of multivariate sliced inverse regression, Journal of Statistical Computation and Simulation, vol.15, issue.1, pp.1-17, 2007.
DOI : 10.1002/env.630

D. F. Andrews and C. L. Mallows, Scale mixtures of normal distributions, Journal of the Royal Statistical Society. Series B (Methodological), vol.36, pp.99-102, 1974.

Y. Aragon, A Gauss implementation of multivariate sliced inverse regression, Computational Statistics, vol.12, pp.355-372, 1997.

Y. Aragon and J. Saracco, Sliced inverse regression (SIR): an appraisal of small sample alternatives to slicing, Compitational Statistics, vol.12, pp.109-130, 1997.

R. Azaïs, A. Gégout-petit, and A. J. Saracco, Optimal quantization applied to sliced inverse regression, Journal of Statistical Planning and Inference, vol.142, issue.2, pp.481-492, 2012.
DOI : 10.1016/j.jspi.2011.08.006

R. F. Barber and E. J. Candès, Controlling the false discovery rate via knockoffs, The Annals of Statistics, pp.2055-2085, 2015.

L. Barreda, A. Gannoun, and A. J. Saracco, Some extensions of multivariate sliced inverse regression, Journal of Statistical Computation and Simulation, vol.15, issue.1, pp.1-17, 2007.
DOI : 10.1002/env.630

B. Bercu, T. M. Nguyen, and A. J. Saracco, A new approach on recursive and non-recursive SIR methods, Journal of the Korean Statistical Society, vol.41, issue.1, pp.41-58, 2012.
DOI : 10.1016/j.jkss.2011.05.005
URL : https://hal.archives-ouvertes.fr/hal-00642653

C. Bernard-michel, S. Douté, M. Fauvel, L. Gardes, and A. S. Girard, Retrieval of Mars surface physical properties from OMEGA hyperspectral images using regularized sliced inverse regression, Journal of Geophysical Research, vol.20, issue.2, p.114, 2009.
DOI : 10.1137/1.9780898717921
URL : https://hal.archives-ouvertes.fr/hal-00383143

C. Bernard-michel, L. Gardes, and A. S. Girard, Gaussian Regularized Sliced Inverse Regression, Statistics and Computing, vol.5, issue.22, pp.85-98, 2009.
DOI : 10.1137/1.9780898717570
URL : https://hal.archives-ouvertes.fr/inria-00180458

D. R. Brillinger and P. R. Krishnaiah, Handbook of Statistics. Vol. 3: Time Series in the Frequency Domain., Biometrics, vol.42, issue.2, p.1, 1983.
DOI : 10.2307/2531074

M. Chavent, S. Girard, V. Kuentz-simonet, B. Liquet, T. M. Nguyen et al., A sliced inverse regression approach for data stream, Computational Statistics, vol.51, issue.22, pp.29-1129, 2014.
DOI : 10.1093/bioinformatics/bti680
URL : https://hal.archives-ouvertes.fr/hal-00688609

M. Chavent, V. Kuentz, B. Liquet, and A. J. Saracco, A Sliced Inverse Regression Approach for a Stratified Population, Communications in Statistics - Theory and Methods, vol.5, issue.21, pp.40-3857, 2011.
DOI : 10.1214/aos/1032526955

C. Chen and K. Li, Can SIR be as popular as multiple linear regression?, Statistica Sinica, vol.8, pp.289-316, 1998.

A. Chiancone, S. Girard, and A. J. Chanussot, Collaborative sliced inverse regression, Communications in Statistics - Theory and Methods, vol.10, issue.1
DOI : 10.1080/03610920903350531
URL : https://hal.archives-ouvertes.fr/hal-01221010

F. J. Chiaromonte and . Martinelli, Dimension reduction strategies for analyzing global gene expression data with a response, Mathematical Biosciences, vol.176, issue.1, pp.123-144, 2002.
DOI : 10.1016/S0025-5564(01)00106-7

M. Chini, A. Chiancone, and A. S. Stramondo, Scale Object Selection (SOS) through a hierarchical segmentation by a multi-spectral per-pixel classification, Pattern Recognition Letters, vol.49, pp.49-214, 2014.
DOI : 10.1016/j.patrec.2014.07.012
URL : https://hal.archives-ouvertes.fr/hal-01065938

R. Cook, L. Forzani, and A. A. Yao, Necessary and sufficient conditions for consistency of a method for smoothed functional inverse regression, Statistica Sinica, pp.235-238, 2010.

R. D. Cook and . Fisher-lecture, Fisher Lecture: Dimension Reduction in Regression, Statistical Science, vol.22, issue.1, pp.1-26, 2007.
DOI : 10.1214/088342306000000682

R. D. Cook, Reflections on a statistical career and their implications, Past, Present, and Future of Statistical Science, pp.97-108, 2014.
DOI : 10.1201/b16720-12

R. D. Cook-and-l and . Forzani, Likelihood-Based Sufficient Dimension Reduction, Journal of the American Statistical Association, vol.104, issue.485, pp.197-208, 2009.
DOI : 10.1198/jasa.2009.0106

R. D. Cook, L. Forzani, and E. A. , Principal Fitted Components for Dimension Reduction in Regression, Statistical Science, vol.23, issue.4, pp.485-501, 2008.
DOI : 10.1214/08-STS275

R. D. Cook, L. Forzani, and A. D. Tomassi, : A Package for Likelihood-Based Sufficient Dimension Reduction, Journal of Statistical Software, vol.39, issue.3, pp.1-20, 2011.
DOI : 10.18637/jss.v039.i03

R. D. Cook-and-c and . Nachtsheim, Reweighting to Achieve Elliptically Contoured Covariates in Regression, Journal of the American Statistical Association, vol.41, issue.426, pp.592-599, 1994.
DOI : 10.1002/0471725218

R. D. Cook-and-s and . Weisberg, Sliced inverse regression for dimension reduction: Comment, Journal of the American Statistical Association, vol.86, pp.328-332, 1991.

R. Coudret, S. Girard, and A. J. Saracco, A new sliced inverse regression method for multivariate response, Computational Statistics & Data Analysis, vol.77, pp.285-299, 2014.
DOI : 10.1016/j.csda.2014.03.006
URL : https://hal.archives-ouvertes.fr/hal-00714981

R. Dennis and . Cook, Save: a method for dimension reduction and graphics in regression , Communications in statistics-Theory and methods, pp.2109-2121, 2000.

P. D. Diaconis and . Freedman, Asymptotics of graphical projection pursuit, The Annals of Statistics, pp.793-815, 1984.

S. Ding and R. D. Cook, Tensor sliced inverse regression, Journal of Multivariate Analysis, vol.133, pp.216-231, 2015.
DOI : 10.1016/j.jmva.2014.08.015

Y. Dong, Z. Yu, and A. L. Zhu, Robust inverse regression for dimension reduction, Journal of Multivariate Analysis, vol.134, pp.71-81, 2015.
DOI : 10.1016/j.jmva.2014.10.005

L. Ferré, Determining the dimension in sliced inverse regression and related methods, Journal of the American Statistical Association, vol.93, pp.132-140, 1998.

L. Ferré and A. Yao, Functional sliced inverse regression analysis, Statistics, vol.3, issue.6, pp.37-475, 2003.
DOI : 10.2307/2291210

K. Fukumizu, F. R. Bach, and A. M. Jordan, Dimensionality reduction for supervised learning with reproducing kernel Hilbert spaces, The Journal of Machine Learning Research, vol.5, pp.73-99, 2004.
DOI : 10.21236/ADA446572

U. Gather, T. Hilker, and A. C. Becker, A Robustified Version of Sliced Inverse Regression, Statistics in Genetics and in the Environmental Sciences, pp.147-157, 2001.
DOI : 10.1007/978-3-0348-8326-9_10

P. Hall-and-k.-c and . Li, On almost linearity of low dimensional projections from high dimensional data, The Annals of Statistics, pp.867-889, 1993.

W. A. Hardle and . Tsybakov, Comment, Journal of the American Statistical Association, vol.86, issue.414, pp.333-335, 1991.
DOI : 10.1080/01621459.1991.10475037

V. Kuentz, B. Liquet, and A. J. Saracco, Bagging Versions of Sliced Inverse Regression, Communications in Statistics - Theory and Methods, vol.13, issue.11, pp.1985-1996, 2010.
DOI : 10.1007/978-1-4612-0795-5
URL : https://hal.archives-ouvertes.fr/hal-00389125

V. J. Kuentz and . Saracco, Cluster-based Sliced Inverse Regression, Journal of the Korean Statistical Society, vol.39, issue.2, pp.251-267, 2010.
DOI : 10.1016/j.jkss.2009.08.004
URL : https://hal.archives-ouvertes.fr/hal-00547252

B. Li and Y. Dong, Dimension reduction for nonelliptically distributed predictors, The Annals of Statistics, pp.1272-1298, 2009.

B. Li, M. K. Kim, and A. N. Altman, On dimension folding of matrix-or array-valued statistical objects, The Annals of Statistics, pp.1094-1121, 2010.

K. Li, Sliced Inverse Regression for Dimension Reduction, Journal of the American Statistical Association, vol.13, issue.414, pp.316-327, 1991.
DOI : 10.1214/aos/1176345514

K. Li, Y. Aragon, K. Shedden, A. C. Thomas, and . Agnan, Dimension Reduction for Multivariate Response Data, Journal of the American Statistical Association, vol.98, issue.461, pp.98-99, 2003.
DOI : 10.1198/016214503388619139

L. Li, Sparse sufficient dimension reduction, Biometrika, vol.94, issue.3, pp.94-603, 2007.
DOI : 10.1093/biomet/asm044

L. Li, R. D. Cook, and A. C. Nachtsheim, Cluster-based estimation for sufficient dimension reduction, Computational Statistics & Data Analysis, vol.47, issue.1, pp.47-175, 2004.
DOI : 10.1016/j.csda.2003.10.017

L. Li and H. Li, Dimension reduction methods for microarrays with application to censored survival data, Bioinformatics, vol.20, issue.18, pp.3406-3412, 2004.
DOI : 10.1093/bioinformatics/bth415

L. Li and C. J. Nachtsheim, Sparse Sliced Inverse Regression, Technometrics, vol.48, issue.4, pp.48-503, 2006.
DOI : 10.1198/004017006000000129

B. J. Liquet and . Saracco, Method, Communications in Statistics - Simulation and Computation, vol.5, issue.6, pp.1198-1218, 2008.
DOI : 10.1214/aos/1032526955
URL : https://hal.archives-ouvertes.fr/hal-00646593

H. Lue, Sliced inverse regression for multivariate response regression, Journal of Statistical Planning and Inference, vol.139, issue.8, pp.2656-2664, 2009.
DOI : 10.1016/j.jspi.2008.12.006

R. Luo, H. Wang, and A. Tsai, Contour projected dimension reduction, The Annals of Statistics, pp.3743-3778, 2009.

M. Mangel and F. J. Samaniego, Abraham Wald's Work on Aircraft Survivability, Journal of the American Statistical Association, vol.75, issue.386, pp.259-267, 1984.
DOI : 10.1073/pnas.72.1.20

J. Saracco, An asymptotic theory for sliced inverse regression, Communications in Statistics - Theory and Methods, vol.5, issue.9, pp.2141-2171, 1997.
DOI : 10.1214/aos/1176345514

J. R. Schott, Determining the Dimensionality in Sliced Inverse Regression, Journal of the American Statistical Association, vol.16, issue.425, pp.141-148, 1994.
DOI : 10.1214/aos/1176345514

L. Scrucca, Regularized sliced inverse regression with applications in classification, in Data Analysis, Classification and the Forward Search, pp.59-66, 2006.

S. J. Sheather and J. W. Mckean, A comparison of procedures based on inverse regression, Lecture Notes-Monograph Series, pp.31-271, 1997.

S. M. Stigler, Gauss and the invention of least squares, The Annals of Statistics, pp.465-474, 1981.

G. Wang, J. Zhou, W. Wu, and A. M. Chen, Robust functional sliced inverse regression, Statistical Papers, vol.32, issue.1, pp.1-19, 2015.
DOI : 10.1214/aos/1079120132

H. Wang, L. Ni, and A. Tsai, Improving dimension reduction via contourprojection, Statistica Sinica, vol.18, pp.299-311, 2008.

T. Wang, X. M. Wen, and A. L. Zhu, Multiple-population shrinkage estimation via sliced inverse regression, Statistics and Computing, vol.105, issue.1, pp.1-12, 2015.
DOI : 10.1198/jasa.2010.tm09666

Q. Wu, Regularized sliced inverse regression for kernel models, 2007.

X. Xu, C. Ren, R. Wu, and A. H. Yan, Sliced Inverse Regression With Adaptive Spectral Sparsity for Dimension Reduction, IEEE Transactions on Cybernetics, vol.47, issue.3, pp.1-13, 2016.
DOI : 10.1109/TCYB.2016.2526630

Y. Yeh, S. Huang, and A. Y. Lee, Nonlinear dimension reduction with kernel sliced inverse regression, IEEE Transactions on Knowledge and Data Engineering, vol.21, pp.1590-1603, 2009.

X. Yin, B. Li, and A. R. Cook, Successive direction extraction for estimating the central subspace in a multiple-index regression, Journal of Multivariate Analysis, vol.99, issue.8, pp.99-1733, 2008.
DOI : 10.1016/j.jmva.2008.01.006

W. Zhong, P. Zeng, P. Ma, J. S. Liu, and A. Y. Zhu, RSIR: regularized sliced inverse regression for motif discovery, Bioinformatics, vol.21, issue.22, pp.4169-4175, 2005.
DOI : 10.1093/bioinformatics/bti680

J. Zhou, Robust dimension reduction based on canonical correlation, Journal of Multivariate Analysis, vol.100, issue.1, pp.195-209, 2009.
DOI : 10.1016/j.jmva.2008.04.003

L. Zhu-and-z and . Yu, On spline approximation of sliced inverse regression, Science in China Series A: Mathematics, vol.50, pp.1289-1302, 2007.

L. Zhu, K. Fang, and E. A. , Asymptotics for kernel estimate of sliced inverse regression, The Annals of Statistics, pp.1053-1068, 1996.

L. Zhu and K. W. Ng, Asymptotics of sliced inverse regression, Statistica Sinica, vol.5, pp.727-736, 1995.