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Robust estimators of high order derivatives of regression functions

Author

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  • Boente, Graciela
  • Rodriguez, Daniela

Abstract

We consider robust estimates for the derivatives of order [nu] of the regression function. Uniform consistency, allowing to construct a robust data-driven bandwidth, and asymptotically normality results are established. The asymptotic efficiency of the proposed estimates is that of the related M-estimators.

Suggested Citation

  • Boente, Graciela & Rodriguez, Daniela, 2006. "Robust estimators of high order derivatives of regression functions," Statistics & Probability Letters, Elsevier, vol. 76(13), pages 1335-1344, July.
    • Handle: RePEc:eee:stapro:v:76:y:2006:i:13:p:1335-1344
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    Cited by:

    1. Boente, Graciela & Rodriguez, Daniela, 2008. "Robust bandwidth selection in semiparametric partly linear regression models: Monte Carlo study and influential analysis," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2808-2828, January.
    2. Omar Fetitah & Mohammed Kadi Attouch & Salah Khardani & Ali Righi, 2023. "Robust nonparametric equivariant regression for functional data with responses missing at random," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(8), pages 899-929, November.
    3. Azzedine, Nadjia & Laksaci, Ali & Ould-Saïd, Elias, 2008. "On robust nonparametric regression estimation for a functional regressor," Statistics & Probability Letters, Elsevier, vol. 78(18), pages 3216-3221, December.
    4. Zhao, Ge & Ma, Yanyuan, 2016. "Robust nonparametric kernel regression estimator," Statistics & Probability Letters, Elsevier, vol. 116(C), pages 72-79.
    5. Mohamed Lemdani & Elias Ould Saïd, 2017. "Nonparametric robust regression estimation for censored data," Statistical Papers, Springer, vol. 58(2), pages 505-525, June.

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