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On the robustification of the kernel estimator of the functional modal regression

Author

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  • Amel, Azzi
  • Ali, Laksaci
  • Elias, Ould Saïd

Abstract

A new nonparametric estimator of the conditional mode when the regressors are functionals is proposed. The main aim of this paper is to establish the almost complete convergence (with rate) of the constructed estimator is estimate under general assumptions in nonparametric functional statistics. A simulation study is carried out to examine, illustrate, the finite samples behavior of the constructed estimator. Finally, a discussion highlighting the impact of this new estimator in nonparametric functional data analysis is also given.

Suggested Citation

  • Amel, Azzi & Ali, Laksaci & Elias, Ould Saïd, 2022. "On the robustification of the kernel estimator of the functional modal regression," Statistics & Probability Letters, Elsevier, vol. 181(C).
    • Handle: RePEc:eee:stapro:v:181:y:2022:i:c:s0167715221002182
    DOI: 10.1016/j.spl.2021.109256
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    References listed on IDEAS

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    1. Germán Aneiros & Ricardo Cao & Philippe Vieu, 2019. "Editorial on the special issue on Functional Data Analysis and Related Topics," Computational Statistics, Springer, vol. 34(2), pages 447-450, June.
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    4. M'hamed Ezzahrioui & Elias Ould-Saïd, 2008. "Asymptotic normality of a nonparametric estimator of the conditional mode function for functional data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 20(1), pages 3-18.
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