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Discovering model structure for partially linear models

Author

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  • Xin He

    (Shanghai University of Finance and Economics)

  • Junhui Wang

    (City University of Hong Kong)

Abstract

Partially linear models (PLMs) have been widely used in statistical modeling, where prior knowledge is often required on which variables have linear or nonlinear effects in the PLMs. In this paper, we propose a model-free structure selection method for the PLMs, which aims to discover the model structure in the PLMs through automatically identifying variables that have linear or nonlinear effects on the response. The proposed method is formulated in a framework of gradient learning, equipped with a flexible reproducing kernel Hilbert space. The resultant optimization task is solved by an efficient proximal gradient descent algorithm. More importantly, the asymptotic estimation and selection consistencies of the proposed method are established without specifying any explicit model assumption, which assure that the true model structure in the PLMs can be correctly identified with high probability. The effectiveness of the proposed method is also supported by a variety of simulated and real-life examples.

Suggested Citation

  • Xin He & Junhui Wang, 2020. "Discovering model structure for partially linear models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(1), pages 45-63, February.
  • Handle: RePEc:spr:aistmt:v:72:y:2020:i:1:d:10.1007_s10463-018-0682-9
    DOI: 10.1007/s10463-018-0682-9
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    1. Yishay Yafeh & Oved Yosha, 2003. "Large Shareholders and Banks: Who Monitors and How?," Economic Journal, Royal Economic Society, vol. 113(484), pages 128-146, January.
    2. Fan, Jianqing & Feng, Yang & Song, Rui, 2011. "Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Additive Models," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 544-557.
    3. Jianqing Fan & Jinchi Lv, 2008. "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 849-911, November.
    4. Zhang, Hao Helen & Cheng, Guang & Liu, Yufeng, 2011. "Linear or Nonlinear? Automatic Structure Discovery for Partially Linear Models," Journal of the American Statistical Association, American Statistical Association, vol. 106(495), pages 1099-1112.
    5. Yichao Wu & Leonard A. Stefanski, 2015. "Automatic structure recovery for additive models," Biometrika, Biometrika Trust, vol. 102(2), pages 381-395.
    6. Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67, February.
    7. Richard Schmalensee & Thomas M. Stoker, 1999. "Household Gasoline Demand in the United States," Econometrica, Econometric Society, vol. 67(3), pages 645-662, May.
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