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On the convergence rate of maximal deviation distribution for kernel regression estimates

Author

Listed:
  • Konakov, V. D.
  • Piterbarg, V. I.

Abstract

Let (X, Y), X [set membership, variant] Rp, Y [set membership, variant] R1 have the regression function r(x) = E(Y|X = x). We consider the kernel nonparametric estimate rn(x) of r(x) and obtain a sequence of distribution functions approximating the distribution of the maximal deviation with power rate. It is shown that the distribution of the maximal deviation tends to double exponent (which is a conventional form of such theorems) with logarithmic rate and this rate cannot be improved.

Suggested Citation

  • Konakov, V. D. & Piterbarg, V. I., 1984. "On the convergence rate of maximal deviation distribution for kernel regression estimates," Journal of Multivariate Analysis, Elsevier, vol. 15(3), pages 279-294, December.
    • Handle: RePEc:eee:jmvana:v:15:y:1984:i:3:p:279-294
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    Citations

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    Cited by:

    1. Sokbae Lee & Ryo Okui & Yoon†Jae Whang, 2017. "Doubly robust uniform confidence band for the conditional average treatment effect function," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(7), pages 1207-1225, November.
    2. Victor Chernozhukov & Denis Chetverikov & Kengo Kato, 2012. "Gaussian approximation of suprema of empirical processes," CeMMAP working papers CWP44/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Paul Deheuvels & David Mason, 2004. "General Asymptotic Confidence Bands Based on Kernel-type Function Estimators," Statistical Inference for Stochastic Processes, Springer, vol. 7(3), pages 225-277, October.
    4. Néstor Aguilera & Liliana Forzani & Pedro Morin, 2011. "On uniform consistent estimators for convex regression," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(4), pages 897-908.
    5. Qiao, Wanli, 2021. "Extremes of locally stationary Gaussian and chi fields on manifolds," Stochastic Processes and their Applications, Elsevier, vol. 133(C), pages 166-192.
    6. Ali Al-Sharadqah & Majid Mojirsheibani & William Pouliot, 2020. "On the performance of weighted bootstrapped kernel deconvolution density estimators," Statistical Papers, Springer, vol. 61(4), pages 1773-1798, August.
    7. Mojirsheibani, Majid, 2012. "A weighted bootstrap approximation of the maximal deviation of kernel density estimates over general compact sets," Journal of Multivariate Analysis, Elsevier, vol. 112(C), pages 230-241.
    8. Mojirsheibani, Majid, 2021. "A note on the performance of bootstrap kernel density estimation with small re-sample sizes," Statistics & Probability Letters, Elsevier, vol. 178(C).
    9. Enno Mammen, "undated". "Comparing nonparametric versus parametric regression fits," Statistic und Oekonometrie 9205, Humboldt Universitaet Berlin.
    10. Rob Euwals & Bertrand Melenberg & Arthur van Soest, 1998. "Testing the predictive value of subjective labour supply data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 13(5), pages 567-585.
    11. Majid Mojirsheibani, 2022. "On the maximal deviation of kernel regression estimators with NMAR response variables," Statistical Papers, Springer, vol. 63(5), pages 1677-1705, October.
    12. Katharina Proksch, 2016. "On confidence bands for multivariate nonparametric regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 68(1), pages 209-236, February.
    13. Ali Al-Sharadqah & Majid Mojirsheibani, 2020. "A simple approach to construct confidence bands for a regression function with incomplete data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(1), pages 81-99, March.

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