Toward the Notion of Intrinsically Linear Models in Nonlinear Regression

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Toward the Notion of Intrinsically Linear Models in Nonlinear Regression. / Linke, Yu Yu ; Borisov, I. S.

In: Siberian Advances in Mathematics, Vol. 29, No. 3, 01.07.2019, p. 210-216.

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

Harvard

Linke, YY & Borisov, IS 2019, 'Toward the Notion of Intrinsically Linear Models in Nonlinear Regression', Siberian Advances in Mathematics, vol. 29, no. 3, pp. 210-216. https://doi.org/10.3103/S1055134419030064

APA

Linke, Y. Y., & Borisov, I. S. (2019). Toward the Notion of Intrinsically Linear Models in Nonlinear Regression. Siberian Advances in Mathematics, 29(3), 210-216. https://doi.org/10.3103/S1055134419030064

Vancouver

Linke YY , Borisov IS. Toward the Notion of Intrinsically Linear Models in Nonlinear Regression. Siberian Advances in Mathematics. 2019 Jul 1;29(3):210-216. doi: 10.3103/S1055134419030064

Author

Linke, Yu Yu ; Borisov, I. S. / Toward the Notion of Intrinsically Linear Models in Nonlinear Regression. In: Siberian Advances in Mathematics. 2019 ; Vol. 29, No. 3. pp. 210-216.

BibTeX

@article{93365b9a8c0241d9bacbbefc4bf05841,

title = "Toward the Notion of Intrinsically Linear Models in Nonlinear Regression",

abstract = "We discuss a refinement of the notion of intrinsical linearity of nonlinear regression models. We show that some known nonlinear regression models satisfy this definition. This fact allows us to find the shortest way to construct consistent (or asymptotically normal) estimators for the parameters of such models. Moreover, we show that one of the known previous interpretations of this notion may lead to inconsistent estimation procedures.",

keywords = "explicit estimator, intrinsically linear model, nonlinear regression",

author = "Linke, {Yu Yu} and Borisov, {I. S.}",

note = "Publisher Copyright: {\textcopyright} 2019, Allerton Press, Inc.",

year = "2019",

month = jul,

day = "1",

doi = "10.3103/S1055134419030064",

language = "English",

volume = "29",

pages = "210--216",

journal = "Siberian Advances in Mathematics",

issn = "1055-1344",

publisher = "PLEIADES PUBLISHING INC",

number = "3",

}

RIS

TY - JOUR

T1 - Toward the Notion of Intrinsically Linear Models in Nonlinear Regression

AU - Linke, Yu Yu

AU - Borisov, I. S.

PY - 2019/7/1

Y1 - 2019/7/1

N2 - We discuss a refinement of the notion of intrinsical linearity of nonlinear regression models. We show that some known nonlinear regression models satisfy this definition. This fact allows us to find the shortest way to construct consistent (or asymptotically normal) estimators for the parameters of such models. Moreover, we show that one of the known previous interpretations of this notion may lead to inconsistent estimation procedures.

AB - We discuss a refinement of the notion of intrinsical linearity of nonlinear regression models. We show that some known nonlinear regression models satisfy this definition. This fact allows us to find the shortest way to construct consistent (or asymptotically normal) estimators for the parameters of such models. Moreover, we show that one of the known previous interpretations of this notion may lead to inconsistent estimation procedures.

KW - explicit estimator

KW - intrinsically linear model

KW - nonlinear regression

UR - http://www.scopus.com/inward/record.url?scp=85071618647&partnerID=8YFLogxK

U2 - 10.3103/S1055134419030064

DO - 10.3103/S1055134419030064

M3 - Article

AN - SCOPUS:85071618647

VL - 29

SP - 210

EP - 216

JO - Siberian Advances in Mathematics

JF - Siberian Advances in Mathematics

SN - 1055-1344

IS - 3

ER -

ID: 21472316