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Efficient dimension reduction for multivariate response data

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  • Zhang, Yaowu
  • Zhu, Liping
  • Ma, Yanyuan

Abstract

We propose a semiparametric approach to reduce the covariate dimension for multivariate response data. The method bypasses the conventional inverse regression procedure hence seamlessly avoids the potential difficulties related to the dimension of the response. In addition, coupled with a proper parameterization, the approach allows for statistical inference of the dimension reduction subspace for a wide range of models. The resultant estimator is shown to be root-n consistent, asymptotically normal and semiparametrically efficient. The efficiency gain of the semiparametric approach is significant in both simulations and an application to a primary hypertension study conducted in PR China.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jmvana:v:155:y:2017:i:c:p:187-199
    DOI: 10.1016/j.jmva.2017.01.001
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    References listed on IDEAS

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    1. Jae Keun Yoo & R. Dennis Cook, 2007. "Optimal sufficient dimension reduction for the conditional mean in multivariate regression," Biometrika, Biometrika Trust, vol. 94(1), pages 231-242.
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    3. Heng-Hui Lue, 2010. "On principal Hessian directions for multivariate response regressions," Computational Statistics, Springer, vol. 25(4), pages 619-632, December.
    4. 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.
    5. Yingcun Xia & Howell Tong & W. K. Li & Li‐Xing Zhu, 2002. "An adaptive estimation of dimension reduction space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 363-410, August.
    6. Li, Qi, 2000. "Efficient Estimation of Additive Partially Linear Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 41(4), pages 1073-1092, November.
    7. Yanyuan Ma & Liping Zhu, 2014. "On estimation efficiency of the central mean subspace," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(5), pages 885-901, November.
    8. Hardle, Wolfgang & LIang, Hua & Gao, Jiti, 2000. "Partially linear models," MPRA Paper 39562, University Library of Munich, Germany, revised 01 Sep 2000.
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    Cited by:

    1. Yu-Zhu Tian & Man-Lai Tang & Mao-Zai Tian, 2021. "Bayesian joint inference for multivariate quantile regression model with L $$_{1/2}$$ 1 / 2 penalty," Computational Statistics, Springer, vol. 36(4), pages 2967-2994, December.
    2. Girard, Stéphane & Lorenzo, Hadrien & Saracco, Jérôme, 2022. "Advanced topics in Sliced Inverse Regression," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    3. 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).

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