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Teoriya Veroyatnostei i ee Primeneniya, 2018, Volume 63, Issue 1, Pages 29–56
DOI: https://doi.org/10.4213/tvp5155 (Mi tvp5155) 		 
This article is cited in 18 scientific papers (total in 18 papers)

Constructing explicit estimators in nonlinear regression problems

Yu. Yu. Linkeab, I. S. Borisovab

a Sobolev Institute of Mathematics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk
b Novosibirsk State University
Full-text PDF (664 kB) Citations (18)
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DOI: https://doi.org/10.4213/tvp5155
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: nonlinear regression, explicit estimator, $\alpha_n$-consistency, asymptotic normality, one-step estimator, initial estimator.
Funding agency Grant number
Russian Foundation for Basic Research 18-01-00074
This work was partially supported by the Russian Foundation for Basic Research (grant 18-01-00074).
Received: 24.02.2016
Accepted: 22.05.2017
English version:
Theory of Probability and its Applications, 2018, Volume 63, Issue 1, Pages 22–44
DOI: https://doi.org/10.1137/S0040585X97T988897
Bibliographic databases:
Document Type: Article
Language: Russian
Citation: Yu. Yu. Linke, I. S. Borisov, “Constructing explicit estimators in nonlinear regression problems”, Teor. Veroyatnost. i Primenen., 63:1 (2018), 29–56; Theory Probab. Appl., 63:1 (2018), 22–44
Citation in format AMSBIB
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    • This publication is cited in the following 18 articles:
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
    		Теория вероятностей и ее применения Theory of Probability and its Applications
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