Universal kernel-type estimators for the conditional variance in heteroscedastic models of nonparametric regression Full article
| Journal |
Siberian Advances in Mathematics
ISSN: 1055-1344 , E-ISSN: 1934-8126 |
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| Output data | Year: 2025, Volume: 35, Number: 4, Pages: 277-286 Pages count : 10 DOI: 10.1134/S1055134425040017 | ||
| Tags | nonparametric heteroscedastic regression, kernel estimator, conditional variance function, consistency, fixed design, random design, strongly dependent design | ||
| Authors |
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| Affiliations |
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Funding (1)
| 1 | Sobolev Institute of Mathematics | FWNF-2024-0001 |
Abstract:
The consistency of new universal kernel estimators for conditional variance function
in a heteroscedastic nonparametric regression model has been proven. The new estimators are
insensitive to the nature of the design dependence. For design that can be either fixed or random,
only the following condition is used: the design points densely fill the domain of regression function.
As a consequence, we consider the problem of constructing a confidence region for a regression
function under the above-mentioned very general conditions on the design in terms of dense data.
Cite:
Borisov I.S.
, Linke Y.Y.
Universal kernel-type estimators for the conditional variance in heteroscedastic models of nonparametric regression
Siberian Advances in Mathematics. 2025. V.35. N4. P.277-286. DOI: 10.1134/S1055134425040017 Scopus
Universal kernel-type estimators for the conditional variance in heteroscedastic models of nonparametric regression
Siberian Advances in Mathematics. 2025. V.35. N4. P.277-286. DOI: 10.1134/S1055134425040017 Scopus
Dates:
| Submitted: | Sep 8, 2025 |
| Accepted: | Oct 29, 2025 |
| Published online: | Nov 19, 2025 |
Identifiers:
| Scopus: | 2-s2.0-105025421912 |
Citing:
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