Turkish Journal of Computer and Mathematics Education
Journal license

Journal

Turkish Journal of Computer and Mathematics Education


Volume
& Issue

Volume 14, Issue 2


Published
on

March 8, 2023


Pages

159-179


DOI

Article

Robust equivariant nonparametric regression estimators for strongly mixing data using a k nearest neighbour approach


Authors

Somia Guenani Affiliation:
Lab. of Statistics and Process Stochastic, University Djillali Liabes, BP 89 Sidi Bel Abbes 22000, Algeria
, Wahiba Bouabsa Affiliation:
Lab. of Statistics and Process Stochastic, University Djillali Liabes, BP 89 Sidi Bel Abbes 22000, Algeria
and Mohammed Kadi Attouch Affiliation:
Lab. of Statistics and Process Stochastic, University Djillali Liabes, BP 89 Sidi Bel Abbes 22000, Algeria


Abstract

We discuss in this paper the robust equivariant nonparametric regression estimators for strong mixing data with the k Nearest Neighbour (kNN) method. We consider a new robust regression estimator when the scale parameter is unknown. The principal aim is to prove the almost complete convergence (with rate) for the proposed estimator. Furthermore, a comparison study based on simulated data is also provided to illustrate the finite sample performances and the usefulness of the kNN approach.


Keywords

Functional data, ergodic data, kNN estimation, kernel estimate, uniform almost complete convergence rate, entropy


Citation

Guenani, S., Bouabsa, W., & Attouch, M. K. (2023). Robust equivariant nonparametric regression estimators for strongly mixing data using a k nearest neighbour approach. Turkish Journal of Computer and Mathematics Education, 14(2), 159–179.

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