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Robust nonparametric equivariant regression for functional data with responses missing at random

Author

Listed:
  • Omar Fetitah

    (University of Djillali Liabes)

  • Mohammed Kadi Attouch

    (University of Djillali Liabes)

  • Salah Khardani

    (Faculté des Sciences de Tunis El Manar)

  • Ali Righi

    (University of Djillali Liabes)

Abstract

The paper deal with the robust equivariant nonparametric regression when the covariates are functional and the response variables are missing at random (MAR). Under some mild conditions, the almost complete convergence rate of the proposed estimators for both cases known and unknown scale parameter are established. Some simulations study are drawing, and real data analysis are given to illustrate the higher predictive performances of our proposed method.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:metrik:v:86:y:2023:i:8:d:10.1007_s00184-023-00898-1
    DOI: 10.1007/s00184-023-00898-1
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    References listed on IDEAS

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    5. Collomb, Gérard & Härdle, Wolfgang, 1986. "Strong uniform convergence rates in robust nonparametric time series analysis and prediction: Kernel regression estimation from dependent observations," Stochastic Processes and their Applications, Elsevier, vol. 23(1), pages 77-89, October.
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