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An ensemble of inverse moment estimators for sufficient dimension reduction

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  • Wang, Qin
  • Xue, Yuan

Abstract

Sufficient dimension reduction (SDR) is known to be a useful tool in data visualization and information retrieval for high dimensional data. Many well-known SDR approaches investigate the inverse conditional moments of the predictors given the response. Motivated by the idea of the aggregate dimension reduction, we propose an ensemble of inverse moment estimators to explore the central subspace. The new approach can substantially improve the estimation accuracy for the directions beyond the regression mean functions. A ladle estimator is proposed to determine the structural dimension of the central subspace. We further present two variable selection procedures to improve the interpretability of the reduced variables. Both simulation studies and a real data application show the efficacy of the newly proposed method.

Suggested Citation

  • Wang, Qin & Xue, Yuan, 2021. "An ensemble of inverse moment estimators for sufficient dimension reduction," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    • Handle: RePEc:eee:csdana:v:161:y:2021:i:c:s016794732100075x
    DOI: 10.1016/j.csda.2021.107241
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    References listed on IDEAS

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    Cited by:

    1. Wenjuan Li & Wenying Wang & Jingsi Chen & Weidong Rao, 2023. "Aggregate Kernel Inverse Regression Estimation," Mathematics, MDPI, vol. 11(12), pages 1-10, June.
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