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Cross-validation and non-parametric k nearest-neighbour estimation


  • Desheng Ouyang
  • Dong Li
  • Qi Li


In this paper we consider the problem of estimating a non-parametric regression function using the k nearest-neighbour method. We provide asymptotic theories for the least-squares cross validation (CV) selected smoothing parameter k for both local constant and local linear estimation methods. We also establish the asymptotic normality results for the resulting non-parametric regression function estimators. Some limited Monte Carlo experiments show that the CV method performs well in finite sample applications. Copyright Royal Economic Society 2006

Suggested Citation

  • Desheng Ouyang & Dong Li & Qi Li, 2006. "Cross-validation and non-parametric k nearest-neighbour estimation," Econometrics Journal, Royal Economic Society, vol. 9(3), pages 448-471, November.
  • Handle: RePEc:ect:emjrnl:v:9:y:2006:i:3:p:448-471

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    1. repec:ebl:ecbull:v:3:y:2008:i:48:p:1-6 is not listed on IDEAS
    2. repec:hal:journl:halshs-00423871 is not listed on IDEAS
    3. David Jacho-Chávez, 2008. "k nearest-neighbor estimation of inverse density weighted expectations," Economics Bulletin, AccessEcon, vol. 3(48), pages 1-6.
    4. repec:hal:journl:halshs-00511979 is not listed on IDEAS
    5. Li, Hongjun & Li, Qi & Liu, Ruixuan, 2016. "Consistent model specification tests based on k-nearest-neighbor estimation method," Journal of Econometrics, Elsevier, vol. 194(1), pages 187-202.

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