On Sufficient Conditions for the Consistency of Local Linear Kernel Estimators Full article
| Journal |
Mathematical Notes
ISSN: 0001-4346 , E-ISSN: 1573-8876 |
||
|---|---|---|---|
| Output data | Year: 2023, Volume: 114, Number: 3-4, Pages: 308-321 Pages count : 14 DOI: 10.1134/s0001434623090043 | ||
| Tags | nonparametric regression, local linear estimator, uniform consistency, fixed design, random design, highly dependent design elements | ||
| Authors |
|
||
| Affiliations |
|
Funding (1)
| 1 | Sobolev Institute of Mathematics | FWNF-2022-0015 |
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.
Cite:
Linke Y.Y.
On Sufficient Conditions for the Consistency of Local Linear Kernel Estimators
Mathematical Notes. 2023. V.114. N3-4. P.308-321. DOI: 10.1134/s0001434623090043 WOS Scopus РИНЦ OpenAlex
On Sufficient Conditions for the Consistency of Local Linear Kernel Estimators
Mathematical Notes. 2023. V.114. N3-4. P.308-321. DOI: 10.1134/s0001434623090043 WOS Scopus РИНЦ OpenAlex
Original:
Линке Ю.Ю.
О достаточных условиях состоятельности локально-линейных ядерных оценок
Математические заметки. 2023. Т.114. №3. С.353-369. DOI: 10.4213/mzm13906 РИНЦ OpenAlex
О достаточных условиях состоятельности локально-линейных ядерных оценок
Математические заметки. 2023. Т.114. №3. С.353-369. DOI: 10.4213/mzm13906 РИНЦ OpenAlex
Dates:
| Submitted: | Jan 29, 2023 |
| Accepted: | Mar 15, 2023 |
| Published print: | Oct 24, 2023 |
| Published online: | Oct 24, 2023 |
Identifiers:
| Web of science: | WOS:001089580100004 |
| Scopus: | 2-s2.0-85174583020 |
| Elibrary: | 63812436 |
| OpenAlex: | W4387902273 |