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A robust penalized estimation for identification in semiparametric additive models

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  • Yang, Jing
  • Yang, Hu

Abstract

Based on the modal regression estimation (Yao et al., 2012) and spline based estimation method, we provide a novel and robust approach to identify nonzero components as well as linear components in semiparametric additive models by applying a two-fold smoothly clipped absolute deviation (Fan and Li, 2001) penalty. The performance of the new method is demonstrated in terms of theoretical and simulation results.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:stapro:v:110:y:2016:i:c:p:268-277
    DOI: 10.1016/j.spl.2015.10.002
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    References listed on IDEAS

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    1. Heng Lian, 2012. "Semiparametric Estimation of Additive Quantile Regression Models by Two-Fold Penalty," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 337-350, March.
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    7. Wang, Mingqiu & Song, Lixin, 2013. "Identification for semiparametric varying coefficient partially linear models," Statistics & Probability Letters, Elsevier, vol. 83(5), pages 1311-1320.
    8. Yang, Hu & Guo, Chaohui & Lv, Jing, 2014. "A robust and efficient estimation method for single-index varying-coefficient models," Statistics & Probability Letters, Elsevier, vol. 94(C), pages 119-127.
    9. Lv, Zhike & Zhu, Huiming & Yu, Keming, 2014. "Robust variable selection for nonlinear models with diverging number of parameters," Statistics & Probability Letters, Elsevier, vol. 91(C), pages 90-97.
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