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On a Projective Resampling Method for Dimension Reduction With Multivariate Responses

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  • Li, Bing
  • Wen, Songqiao
  • Zhu, Lixing

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  • Li, Bing & Wen, Songqiao & Zhu, Lixing, 2008. "On a Projective Resampling Method for Dimension Reduction With Multivariate Responses," Journal of the American Statistical Association, American Statistical Association, vol. 103(483), pages 1177-1186.
    • Handle: RePEc:bes:jnlasa:v:103:i:483:y:2008:p:1177-1186
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    Citations

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    Cited by:

    1. 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.
    2. Zeng, Bilin & Yu, Zhou & Wen, Xuerong Meggie, 2015. "A note on cumulative mean estimation," Statistics & Probability Letters, Elsevier, vol. 96(C), pages 322-327.
    3. Xue, Yuan & Yin, Xiangrong & Jiang, Xiaolin, 2016. "Ensemble sufficient dimension folding methods for analyzing matrix-valued data," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 193-205.
    4. Heng-Hui Lue, 2010. "On principal Hessian directions for multivariate response regressions," Computational Statistics, Springer, vol. 25(4), pages 619-632, December.
    5. Jae Yoo & Keunbaik Lee & Seongho Wu, 2010. "On the extension of sliced average variance estimation to multivariate regression," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 19(4), pages 529-540, November.
    6. Xie, Chuanlong & Zhu, Lixing, 2019. "A goodness-of-fit test for variable-adjusted models," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 27-48.
    7. Luo, Chongliang & Liang, Jian & Li, Gen & Wang, Fei & Zhang, Changshui & Dey, Dipak K. & Chen, Kun, 2018. "Leveraging mixed and incomplete outcomes via reduced-rank modeling," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 378-394.
    8. Iaci, Ross & Yin, Xiangrong & Zhu, Lixing, 2016. "The Dual Central Subspaces in dimension reduction," Journal of Multivariate Analysis, Elsevier, vol. 145(C), pages 178-189.
    9. Hilafu, Haileab & Yin, Xiangrong, 2013. "Sufficient dimension reduction in multivariate regressions with categorical predictors," Computational Statistics & Data Analysis, Elsevier, vol. 63(C), pages 139-147.
    10. Zhang, Wei & Gao, Wei & Ng, Hon Keung Tony, 2023. "Multivariate tests of independence based on a new class of measures of independence in Reproducing Kernel Hilbert Space," Journal of Multivariate Analysis, Elsevier, vol. 195(C).
    11. Girard, Stéphane & Lorenzo, Hadrien & Saracco, Jérôme, 2022. "Advanced topics in Sliced Inverse Regression," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    12. Weng, Jiaying, 2022. "Fourier transform sparse inverse regression estimators for sufficient variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    13. 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).
    14. Zhang, Yaowu & Zhu, Liping & Ma, Yanyuan, 2017. "Efficient dimension reduction for multivariate response data," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 187-199.
    15. Hilafu, Haileab & Wu, Wenbo, 2017. "Partial projective resampling method for dimension reduction: With applications to partially linear models," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 1-14.
    16. Wang, Pei & Yin, Xiangrong & Yuan, Qingcong & Kryscio, Richard, 2021. "Feature filter for estimating central mean subspace and its sparse solution," Computational Statistics & Data Analysis, Elsevier, vol. 163(C).
    17. Zhu, Xuehu & Chen, Fei & Guo, Xu & Zhu, Lixing, 2016. "Heteroscedasticity testing for regression models: A dimension reduction-based model adaptive approach," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 263-283.
    18. Sheng, Wenhui & Yin, Xiangrong, 2013. "Direction estimation in single-index models via distance covariance," Journal of Multivariate Analysis, Elsevier, vol. 122(C), pages 148-161.

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