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Robust dimension reduction based on canonical correlation

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  • Zhou, Jianhui

Abstract

The canonical correlation (CANCOR) method for dimension reduction in a regression setting is based on the classical estimates of the first and second moments of the data, and therefore sensitive to outliers. In this paper, we study a weighted canonical correlation (WCANCOR) method, which captures a subspace of the central dimension reduction subspace, as well as its asymptotic properties. In the proposed WCANCOR method, each observation is weighted based on its Mahalanobis distance to the location of the predictor distribution. Robust estimates of the location and scatter, such as the minimum covariance determinant (MCD) estimator of Rousseeuw [P.J. Rousseeuw, Multivariate estimation with high breakdown point, Mathematical Statistics and Applications B (1985) 283-297], can be used to compute the Mahalanobis distance. To determine the number of significant dimensions in WCANCOR, a weighted permutation test is considered. A comparison of SIR, CANCOR and WCANCOR is also made through simulation studies to show the robustness of WCANCOR to outlying observations. As an example, the Boston housing data is analyzed using the proposed WCANCOR method.

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  • Zhou, Jianhui, 2009. "Robust dimension reduction based on canonical correlation," Journal of Multivariate Analysis, Elsevier, vol. 100(1), pages 195-209, January.
    • Handle: RePEc:eee:jmvana:v:100:y:2009:i:1:p:195-209
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    References listed on IDEAS

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    1. Croux, Christophe & Haesbroeck, Gentiane, 1999. "Influence Function and Efficiency of the Minimum Covariance Determinant Scatter Matrix Estimator," Journal of Multivariate Analysis, Elsevier, vol. 71(2), pages 161-190, November.
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    3. Taskinen, Sara & Croux, Christophe & Kankainen, Annaliisa & Ollila, Esa & Oja, Hannu, 2006. "Influence functions and efficiencies of the canonical correlation and vector estimates based on scatter and shape matrices," Journal of Multivariate Analysis, Elsevier, vol. 97(2), pages 359-384, February.
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    Cited by:

    1. Andrea Bergesio & María Eugenia Szretter Noste & Víctor J. Yohai, 2021. "A robust proposal of estimation for the sufficient dimension reduction problem," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 758-783, September.
    2. Guochang Wang & Jianjun Zhou & Wuqing Wu & Min Chen, 2017. "Robust functional sliced inverse regression," Statistical Papers, Springer, vol. 58(1), pages 227-245, March.
    3. Lan Xue & Jing Wang, 2010. "Distribution function estimation by constrained polynomial spline regression," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 22(4), pages 443-457.
    4. Dong, Yuexiao & Yu, Zhou & Zhu, Liping, 2015. "Robust inverse regression for dimension reduction," Journal of Multivariate Analysis, Elsevier, vol. 134(C), pages 71-81.
    5. Chiancone, Alessandro & Forbes, Florence & Girard, Stéphane, 2017. "Student Sliced Inverse Regression," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 441-456.
    6. Cator, Eric A. & Lopuhaä, Hendrik P., 2010. "Asymptotic expansion of the minimum covariance determinant estimators," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2372-2388, November.
    7. Zhou, Jingke & Xu, Wangli & Zhu, Lixing, 2015. "Robust estimating equation-based sufficient dimension reduction," Journal of Multivariate Analysis, Elsevier, vol. 134(C), pages 99-118.
    8. Zhou, Jingke & Zhu, Lixing, 2016. "Principal minimax support vector machine for sufficient dimension reduction with contaminated data," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 33-48.

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