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Constructing explicit estimators in nonlinear regression problems. / Linke, Yu Yu; Borisov, I. S.

In: Theory of Probability and its Applications, Vol. 63, No. 1, 01.01.2018, p. 22-44.

Research output: Contribution to journal › Article › peer-review

Harvard

Linke, YY & Borisov, IS 2018, 'Constructing explicit estimators in nonlinear regression problems', Theory of Probability and its Applications, vol. 63, no. 1, pp. 22-44. https://doi.org/10.1137/S0040585X97T988897

APA

Linke, Y. Y., & Borisov, I. S. (2018). Constructing explicit estimators in nonlinear regression problems. Theory of Probability and its Applications, 63(1), 22-44. https://doi.org/10.1137/S0040585X97T988897

Vancouver

Linke YY, Borisov IS. Constructing explicit estimators in nonlinear regression problems. Theory of Probability and its Applications. 2018 Jan 1;63(1):22-44. doi: 10.1137/S0040585X97T988897

Author

Linke, Yu Yu ; Borisov, I. S. / Constructing explicit estimators in nonlinear regression problems. In: Theory of Probability and its Applications. 2018 ; Vol. 63, No. 1. pp. 22-44.

BibTeX

@article{4b917aa710e14e978d5450028ceb21c4,
title = "Constructing explicit estimators in nonlinear regression problems",
abstract = "In the paper, we propose a general approach to constructing explicit consistent estimators for some classes of nonlinear regression models. These estimators can be used as initial ones in one-step estimation procedures capable of delivering, in a sense, optimal estimators in an explicit form.",
keywords = "Asymptotic normality, Explicit estimator, Initial estimator, Nonlinear regression, One-step estimator, α -consistency",
author = "Linke, {Yu Yu} and Borisov, {I. S.}",
note = "Funding Information: ∗Received by the editors February 24, 2016. This work was partially supported by the Russian Foundation for Basic Research (grant 18-01-00074). Originally published in the Russian journal Teoriya Veroyatnostei i ee Primeneniya, 63 (2018), pp. 29–56. http://www.siam.org/journals/tvp/63-1/T98889.html †Sobolev Institute of Mathematics, Novosibirsk, Russia, and Novosibirsk State University, Novosibirsk, Russia (linke@math.nsc.ru, sibam@math.nsc.ru)",
year = "2018",
month = jan,
day = "1",
doi = "10.1137/S0040585X97T988897",
language = "English",
volume = "63",
pages = "22--44",
journal = "Theory of Probability and its Applications",
issn = "0040-585X",
publisher = "SIAM PUBLICATIONS",
number = "1",

}

RIS

TY - JOUR

T1 - Constructing explicit estimators in nonlinear regression problems

AU - Linke, Yu Yu

AU - Borisov, I. S.

N1 - Funding Information: ∗Received by the editors February 24, 2016. This work was partially supported by the Russian Foundation for Basic Research (grant 18-01-00074). Originally published in the Russian journal Teoriya Veroyatnostei i ee Primeneniya, 63 (2018), pp. 29–56. http://www.siam.org/journals/tvp/63-1/T98889.html †Sobolev Institute of Mathematics, Novosibirsk, Russia, and Novosibirsk State University, Novosibirsk, Russia (linke@math.nsc.ru, sibam@math.nsc.ru)

PY - 2018/1/1

Y1 - 2018/1/1

N2 - In the paper, we propose a general approach to constructing explicit consistent estimators for some classes of nonlinear regression models. These estimators can be used as initial ones in one-step estimation procedures capable of delivering, in a sense, optimal estimators in an explicit form.

AB - In the paper, we propose a general approach to constructing explicit consistent estimators for some classes of nonlinear regression models. These estimators can be used as initial ones in one-step estimation procedures capable of delivering, in a sense, optimal estimators in an explicit form.

KW - Asymptotic normality

KW - Explicit estimator

KW - Initial estimator

KW - Nonlinear regression

KW - One-step estimator

KW - α -consistency

UR - http://www.scopus.com/inward/record.url?scp=85057338140&partnerID=8YFLogxK

U2 - 10.1137/S0040585X97T988897

DO - 10.1137/S0040585X97T988897

M3 - Article

AN - SCOPUS:85057338140

VL - 63

SP - 22

EP - 44

JO - Theory of Probability and its Applications

JF - Theory of Probability and its Applications

SN - 0040-585X

IS - 1

ER -

ID: 26148020