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This article is cited in 1 scientific paper (total in 1 paper)
On Sufficient Conditions for the Consistency of Local Linear Kernel Estimators
Yu. Yu. Linke Sobolev Institute of Mathematics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk
Abstract:
The consistency of classical local linear kernel estimators in nonparametric regression is proved under constraints on design elements (regressors) weaker than those known earlier. The obtained conditions are universal with respect to the stochastic nature of design, which may be both fixed regular and random and is not required to consist of independent or weakly dependent random variables. Sufficient conditions for pointwise and uniform consistency of classical local linear estimators are stated in terms of the asymptotic behavior of the number of design elements in certain neighborhoods of points in the domain of the regression function.
Keywords:
nonparametric regression, local linear estimator, uniform consistency, fixed design, random design, highly dependent design elements.
Received: 29.01.2023 Revised: 21.02.2023
Citation:
Yu. Yu. Linke, “On Sufficient Conditions for the Consistency of Local Linear Kernel Estimators”, Mat. Zametki, 114:3 (2023), 353–369; Math. Notes, 114:3 (2023), 308–321
Linking options:
https://www.mathnet.ru/eng/mzm13906https://doi.org/10.4213/mzm13906 https://www.mathnet.ru/eng/mzm/v114/i3/p353
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