Standard

Asymptotic properties of one-step M-estimators. / Linke, Yuliana.

In: Communications in Statistics - Theory and Methods, Vol. 48, No. 16, 18.08.2019, p. 4096-4118.

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

Harvard

Linke, Y 2019, 'Asymptotic properties of one-step M-estimators', Communications in Statistics - Theory and Methods, vol. 48, no. 16, pp. 4096-4118. https://doi.org/10.1080/03610926.2018.1487982

APA

Linke, Y. (2019). Asymptotic properties of one-step M-estimators. Communications in Statistics - Theory and Methods, 48(16), 4096-4118. https://doi.org/10.1080/03610926.2018.1487982

Vancouver

Linke Y. Asymptotic properties of one-step M-estimators. Communications in Statistics - Theory and Methods. 2019 Aug 18;48(16):4096-4118. doi: 10.1080/03610926.2018.1487982

Author

Linke, Yuliana. / Asymptotic properties of one-step M-estimators. In: Communications in Statistics - Theory and Methods. 2019 ; Vol. 48, No. 16. pp. 4096-4118.

BibTeX

@article{6427797dc7e445aa84603d8a4dc39eb8,
title = "Asymptotic properties of one-step M-estimators",
abstract = "We study the asymptotic behavior of one-step M-estimators based on not necessarily independent identically distributed observations. In particular, we find conditions for asymptotic normality of these estimators. Asymptotic normality of one-step M-estimators is proven under a wide spectrum of constraints on the exactness of initial estimators. We discuss the question of minimal restrictions on the exactness of initial estimators. We also discuss the asymptotic behavior of the solution to an M-equation closest to the parameter under consideration. As an application, we consider some examples of one-step approximation of quasi-likelihood estimators in nonlinear regression.",
keywords = "asymptotic normality, initial estimator, nonlinear regression, One-step M-estimator, REGRESSION, HIGH BREAKDOWN, INFERENCES, MODELS, NORMALITY, BEHAVIOR, ROOTS, EFFICIENT ESTIMATION",
author = "Yuliana Linke",
year = "2019",
month = aug,
day = "18",
doi = "10.1080/03610926.2018.1487982",
language = "English",
volume = "48",
pages = "4096--4118",
journal = "Communications in Statistics - Theory and Methods",
issn = "0361-0926",
publisher = "Taylor and Francis Ltd.",
number = "16",

}

RIS

TY - JOUR

T1 - Asymptotic properties of one-step M-estimators

AU - Linke, Yuliana

PY - 2019/8/18

Y1 - 2019/8/18

N2 - We study the asymptotic behavior of one-step M-estimators based on not necessarily independent identically distributed observations. In particular, we find conditions for asymptotic normality of these estimators. Asymptotic normality of one-step M-estimators is proven under a wide spectrum of constraints on the exactness of initial estimators. We discuss the question of minimal restrictions on the exactness of initial estimators. We also discuss the asymptotic behavior of the solution to an M-equation closest to the parameter under consideration. As an application, we consider some examples of one-step approximation of quasi-likelihood estimators in nonlinear regression.

AB - We study the asymptotic behavior of one-step M-estimators based on not necessarily independent identically distributed observations. In particular, we find conditions for asymptotic normality of these estimators. Asymptotic normality of one-step M-estimators is proven under a wide spectrum of constraints on the exactness of initial estimators. We discuss the question of minimal restrictions on the exactness of initial estimators. We also discuss the asymptotic behavior of the solution to an M-equation closest to the parameter under consideration. As an application, we consider some examples of one-step approximation of quasi-likelihood estimators in nonlinear regression.

KW - asymptotic normality

KW - initial estimator

KW - nonlinear regression

KW - One-step M-estimator

KW - REGRESSION

KW - HIGH BREAKDOWN

KW - INFERENCES

KW - MODELS

KW - NORMALITY

KW - BEHAVIOR

KW - ROOTS

KW - EFFICIENT ESTIMATION

UR - http://www.scopus.com/inward/record.url?scp=85057328032&partnerID=8YFLogxK

U2 - 10.1080/03610926.2018.1487982

DO - 10.1080/03610926.2018.1487982

M3 - Article

AN - SCOPUS:85057328032

VL - 48

SP - 4096

EP - 4118

JO - Communications in Statistics - Theory and Methods

JF - Communications in Statistics - Theory and Methods

SN - 0361-0926

IS - 16

ER -

ID: 17577536