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A note on strong convergence rates in nonparametric regression

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  • Cheng, Philip E.

Abstract

The strong convergence rates in nonparametric regression estimation have been mostly discussed when the error variables in the regression models have finite variances. A few recent studies concern heavy-tailed error distributions for two comparable methods using the kernel and the k-nearest neighbor estimators. The obtained convergence rates are however noncomparable. Assuming the error variables have finite pth moments for the same p, 1

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  • Cheng, Philip E., 1995. "A note on strong convergence rates in nonparametric regression," Statistics & Probability Letters, Elsevier, vol. 24(4), pages 357-364, September.
    • Handle: RePEc:eee:stapro:v:24:y:1995:i:4:p:357-364
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    References listed on IDEAS

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    1. Cheng, Philip E., 1984. "Strong consistency of nearest neighbor regression function estimators," Journal of Multivariate Analysis, Elsevier, vol. 15(1), pages 63-72, August.
    2. Mack, Y. P. & Rosenblatt, M., 1979. "Multivariate k-nearest neighbor density estimates," Journal of Multivariate Analysis, Elsevier, vol. 9(1), pages 1-15, March.
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    Cited by:

    1. Bai, Z. D. & Cheng, Philip E., 2000. "Marcinkiewicz strong laws for linear statistics," Statistics & Probability Letters, Elsevier, vol. 46(2), pages 105-112, January.
    2. Sherwood, Ben, 2016. "Variable selection for additive partial linear quantile regression with missing covariates," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 206-223.

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