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Sliced Inverse Regression with Regularizations

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  • Lexin Li
  • Xiangrong Yin

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  • Lexin Li & Xiangrong Yin, 2008. "Sliced Inverse Regression with Regularizations," Biometrics, The International Biometric Society, vol. 64(1), pages 124-131, March.
    • Handle: RePEc:bla:biomet:v:64:y:2008:i:1:p:124-131
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2007.00836.x
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    References listed on IDEAS

    as
    1. Peide Shi & Chih‐Ling Tsai, 2002. "Regression model selection—a residual likelihood approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 237-252, May.
    2. Li, Lexin & Dennis Cook, R. & Nachtsheim, Christopher J., 2004. "Cluster-based estimation for sufficient dimension reduction," Computational Statistics & Data Analysis, Elsevier, vol. 47(1), pages 175-193, August.
    3. Eaton, Morris L., 1986. "A characterization of spherical distributions," Journal of Multivariate Analysis, Elsevier, vol. 20(2), pages 272-276, December.
    4. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    5. Zhu, Lixing & Miao, Baiqi & Peng, Heng, 2006. "On Sliced Inverse Regression With High-Dimensional Covariates," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 630-643, June.
    6. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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    Citations

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    Cited by:

    1. Persson, Emma & Häggström, Jenny & Waernbaum, Ingeborg & de Luna, Xavier, 2017. "Data-driven algorithms for dimension reduction in causal inference," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 280-292.
    2. Wang, Tao & Zhu, Lixing, 2013. "Sparse sufficient dimension reduction using optimal scoring," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 223-232.
    3. Coudret, R. & Girard, S. & Saracco, J., 2014. "A new sliced inverse regression method for multivariate response," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 285-299.
    4. Wenquan Cui & Jianjun Xu & Yuehua Wu, 2023. "A new reproducing kernel‐based nonlinear dimension reduction method for survival data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(3), pages 1365-1390, September.
    5. Lexin Li & Xiangrong Yin, 2008. "The authors replied as follows:," Biometrics, The International Biometric Society, vol. 64(3), pages 984-986, September.
    6. Xiao, Zhen & Zhang, Qi, 2022. "Dimension reduction for block-missing data based on sparse sliced inverse regression," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
    7. Radchenko, Peter, 2015. "High dimensional single index models," Journal of Multivariate Analysis, Elsevier, vol. 139(C), pages 266-282.
    8. Tan, Xin Lu, 2019. "Optimal estimation of slope vector in high-dimensional linear transformation models," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 179-204.
    9. Fang, Fang & Yu, Zhou, 2020. "Model averaging assisted sufficient dimension reduction," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    10. Emmanuel Jordy Menvouta & Sven Serneels & Tim Verdonck, 2022. "Sparse dimension reduction based on energy and ball statistics," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(4), pages 951-975, December.
    11. Wei Sun & Lexin Li, 2012. "Multiple Loci Mapping via Model-free Variable Selection," Biometrics, The International Biometric Society, vol. 68(1), pages 12-22, March.
    12. Kaida Cai & Xuewen Lu & Hua Shen, 2025. "Group sparse sufficient dimension reduction: a model-free group variable selection method," Computational Statistics, Springer, vol. 40(5), pages 2323-2366, June.
    13. Howard D. Bondell & Lexin Li, 2009. "Shrinkage inverse regression estimation for model‐free variable selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(1), pages 287-299, January.
    14. Girard, Stéphane & Lorenzo, Hadrien & Saracco, Jérôme, 2022. "Advanced topics in Sliced Inverse Regression," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    15. Yang Liu & Francesca Chiaromonte & Bing Li, 2017. "Structured Ordinary Least Squares: A Sufficient Dimension Reduction approach for regressions with partitioned predictors and heterogeneous units," Biometrics, The International Biometric Society, vol. 73(2), pages 529-539, June.
    16. Weng, Jiaying, 2022. "Fourier transform sparse inverse regression estimators for sufficient variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    17. Zhang, Hong-Fan, 2021. "Minimum Average Variance Estimation with group Lasso for the multivariate response Central Mean Subspace," Journal of Multivariate Analysis, Elsevier, vol. 184(C).
    18. C. Bernard‐Michel & L. Gardes & S. Girard, 2008. "A Note on Sliced Inverse Regression with Regularizations," Biometrics, The International Biometric Society, vol. 64(3), pages 982-984, September.
    19. Yoo, Jae Keun, 2013. "Advances in seeded dimension reduction: Bootstrap criteria and extensions," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 70-79.
    20. Wang, Qin & Xue, Yuan, 2021. "An ensemble of inverse moment estimators for sufficient dimension reduction," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    21. Marisa Mariani & Shiquan He & Mark McHugh & Mirko Andreoli & Deep Pandya & Steven Sieber & Zheyang Wu & Paul Fiedler & Shohreh Shahabi & Cristiano Ferlini, 2014. "Integrated Multidimensional Analysis Is Required for Accurate Prognostic Biomarkers in Colorectal Cancer," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-17, July.

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