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Identification for semiparametric varying coefficient partially linear models

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  • Wang, Mingqiu
  • Song, Lixin

Abstract

In this paper, we apply the group smoothly clipped absolute deviation (SCAD) penalty to identify the model structure of the semiparametric varying coefficient partially linear model. The performance of the new approach is demonstrated in terms of the theoretical and numerical results.

Suggested Citation

  • Wang, Mingqiu & Song, Lixin, 2013. "Identification for semiparametric varying coefficient partially linear models," Statistics & Probability Letters, Elsevier, vol. 83(5), pages 1311-1320.
  • Handle: RePEc:eee:stapro:v:83:y:2013:i:5:p:1311-1320
    DOI: 10.1016/j.spl.2013.01.034
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    References listed on IDEAS

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    1. Lian, Heng, 2012. "Shrinkage estimation for identification of linear components in additive models," Statistics & Probability Letters, Elsevier, vol. 82(2), pages 225-231.
    2. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    3. 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.
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    Cited by:

    1. Mingqiu Wang & Peixin Zhao & Xiaoning Kang, 2020. "Structure identification for varying coefficient models with measurement errors based on kernel smoothing," Statistical Papers, Springer, vol. 61(5), pages 1841-1857, October.
    2. Yang, Jing & Yang, Hu, 2016. "A robust penalized estimation for identification in semiparametric additive models," Statistics & Probability Letters, Elsevier, vol. 110(C), pages 268-277.
    3. Jing Yang & Hu Yang & Fang Lu, 2019. "Rank-based shrinkage estimation for identification in semiparametric additive models," Statistical Papers, Springer, vol. 60(4), pages 1255-1281, August.

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