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The k Nearest Neighbors Estimator of the M-Regression in Functional Statistics

Ahmed Bachir1, *, Ibrahim Mufrah Almanjahie1, 2, Mohammed Kadi Attouch3

1 Department of Mathematics, College of Science, King Khalid University, Abha, 62529, Saudi Arabia.
2 Statistical Research and Studies Support Unit, King Khalid University, Abha, 62529, Saudi Arabia.
3 Djillali Liabes University, Sidi Belabbes, 22000, Algeria.

* Corresponding Authors: Ahmed Bachir. Email: ; .

Computers, Materials & Continua 2020, 65(3), 2049-2064.


It is well known that the nonparametric estimation of the regression function is highly sensitive to the presence of even a small proportion of outliers in the data. To solve the problem of typical observations when the covariates of the nonparametric component are functional, the robust estimates for the regression parameter and regression operator are introduced. The main propose of the paper is to consider data-driven methods of selecting the number of neighbors in order to make the proposed processes fully automatic. We use the


Cite This Article

A. Bachir, I. Mufrah Almanjahie and M. Kadi Attouch, "The k nearest neighbors estimator of the m-regression in functional statistics," Computers, Materials & Continua, vol. 65, no.3, pp. 2049–2064, 2020.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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