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An Adaptive Two‐stage Estimation Method for Additive Models

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  • LU LIN
  • XIA CUI
  • LIXING ZHU

Abstract

. In this paper, a two‐stage estimation method for non‐parametric additive models is investigated. Differing from Horowitz and Mammen's two‐stage estimation, our first‐stage estimators are designed not only for dimension reduction but also as initial approximations to all of the additive components. The second‐stage estimators are obtained by using one‐dimensional non‐parametric techniques to refine the first‐stage ones. From this procedure, we can reveal a relationship between the regression function spaces and convergence rate, and then provide estimators that are optimal in the sense that, better than the usual one‐dimensional mean‐squared error (MSE) of the order n−4/5, the MSE of the order n−1 can be achieved when the underlying models are actually parametric. This shows that our estimation procedure is adaptive in a certain sense. Also it is proved that the bandwidth that is selected by cross‐validation depends only on one‐dimensional kernel estimation and maintains the asymptotic optimality. Simulation studies show that the new estimators of the regression function and all components outperform the existing estimators, and their behaviours are often similar to that of the oracle estimator.

Suggested Citation

  • Lu Lin & Xia Cui & Lixing Zhu, 2009. "An Adaptive Two‐stage Estimation Method for Additive Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(2), pages 248-269, June.
    • Handle: RePEc:bla:scjsta:v:36:y:2009:i:2:p:248-269
    DOI: 10.1111/j.1467-9469.2008.00629.x
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    References listed on IDEAS

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    1. Tiejun Tong & Yuedong Wang, 2005. "Estimating residual variance in nonparametric regression using least squares," Biometrika, Biometrika Trust, vol. 92(4), pages 821-830, December.
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    Cited by:

    1. Lin, Lu & Song, Yunquan & Liu, Zhao, 2014. "Local linear–additive estimation for multiple nonparametric regressions," Journal of Multivariate Analysis, Elsevier, vol. 123(C), pages 252-269.
    2. Zhenghui Feng & Lu Lin & Ruoqing Zhu & Lixing Zhu, 2020. "Nonparametric variable selection and its application to additive models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(3), pages 827-854, June.
    3. Feng, Zheng-Hui & Lin, Lu & Zhu, Ruo-Qing & Zhu, Li-Xing, 2018. "Nonparametric Variable Selection and Its Application to Additive Models," IRTG 1792 Discussion Papers 2018-002, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

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