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Bootstrap approximation of nearest neighbor regression function estimates

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  • Dikta, Gerhard

Abstract

Let (X, Y) be a random vector in the plane and denote by m(x) = (YX = x) the corresponding regression function. We show that the bootstrap approximation for the distribution of a smoothed nearest neighbor estimate of m(x) is valid. Also we compare, by Monte Carlo, confidence intervals which are obtained from both the normal and the bootstrap approximation.

Suggested Citation

  • Dikta, Gerhard, 1990. "Bootstrap approximation of nearest neighbor regression function estimates," Journal of Multivariate Analysis, Elsevier, vol. 32(2), pages 213-229, February.
    • Handle: RePEc:eee:jmvana:v:32:y:1990:i:2:p:213-229
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    Cited by:

    1. P. Saha & P. J. Heagerty, 2010. "Time-Dependent Predictive Accuracy in the Presence of Competing Risks," Biometrics, The International Biometric Society, vol. 66(4), pages 999-1011, December.
    2. Zhang, Likun & Castillo, Enrique del & Berglund, Andrew J. & Tingley, Martin P. & Govind, Nirmal, 2020. "Computing confidence intervals from massive data via penalized quantile smoothing splines," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).

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