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Nearest neighbor estimates of regression

Author

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  • Doksum, Kjell A.
  • Jiang, Jiancheng
  • Sun, Bo
  • Wang, Shuzhen

Abstract

New nearest neighbor estimators of the nonparametric regression function and its derivatives are developed. Asymptotic normality is obtained for the proposed estimators over the interior points and the boundary region. Connections with other estimators such as local polynomial smoothers are established. The proposed estimators are boundary adaptive and extensions of the Stute estimators. Asymptotic minimax risk properties are also established for the proposed estimators. Simulations are conducted to compare the performance of the proposed estimators with others.

Suggested Citation

  • Doksum, Kjell A. & Jiang, Jiancheng & Sun, Bo & Wang, Shuzhen, 2017. "Nearest neighbor estimates of regression," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 64-74.
    • Handle: RePEc:eee:csdana:v:110:y:2017:i:c:p:64-74
    DOI: 10.1016/j.csda.2016.12.014
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    References listed on IDEAS

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    1. Cao, Ricardo & Janssen, Paul & Veraverbeke, Noel, 2001. "Relative density estimation and local bandwidth selection for censored data," Computational Statistics & Data Analysis, Elsevier, vol. 36(4), pages 497-510, June.
    2. Yichao Wu & Leonard A. Stefanski, 2015. "Automatic structure recovery for additive models," Biometrika, Biometrika Trust, vol. 102(2), pages 381-395.
    3. 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.
    4. L. A. Stefanski & Yichao Wu & Kyle White, 2014. "Variable Selection in Nonparametric Classification Via Measurement Error Model Selection Likelihoods," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(506), pages 574-589, June.
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