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September 10, 2025Comptes Rendus MathématiqueOpen Access

Rates of strong uniform consistency for the k-nearest neighbors kernel estimators of density and regression function

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The study provides rates of strong uniform consistency for k-nearest neighbors kernel estimators.
Results are applicable in the multivariate case, addressing both density and regression functions.
Strong uniform consistency is examined across the entire space ℝ^p under specific assumptions.
The findings highlight the importance of consistency in statistical estimators for practical applications.

Implication

This analysis reveals rates of strong uniform consistency for k-nearest neighbors estimators in density and regression, pointing to significant implications in multivariate cases.

Abstract

We address the problem of consistency of the k-nearest neighbors kernel estimators of the density and the regression function in the multivariate case. We get the rates of strong uniform consistency on the whole space ℝ p for these estimators under specified assumptions.

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Cite This Study

Mintogo et al. (2025) studied this question.

synapsesocial.com/papers/68c1924e9b7b07f3a0616881 https://doi.org/10.5802/crmath.784

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Implication

This analysis reveals rates of strong uniform consistency for k-nearest neighbors estimators in density and regression, pointing to significant implications in multivariate cases.

Rates of strong uniform consistency for the k-nearest neighbors kernel estimators of density and regression function

Key Points

Implication

Abstract

Discussion

Cite This Study

Rates of strong uniform consistency for the k-nearest neighbors kernel estimators of density and regression function

Key Points

Implication

Abstract

Discussion

Cite This Study

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