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Asymptotic properties of one-step weighted m-estimators with applications to regression. / Linke, Yu Yu.

In: Theory of Probability and its Applications, Vol. 62, No. 3, 01.01.2018, p. 373-398.

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

Harvard

Linke, YY 2018, 'Asymptotic properties of one-step weighted m-estimators with applications to regression', Theory of Probability and its Applications, vol. 62, no. 3, pp. 373-398. https://doi.org/10.1137/S0040585X97T988691

APA

Linke, Y. Y. (2018). Asymptotic properties of one-step weighted m-estimators with applications to regression. Theory of Probability and its Applications, 62(3), 373-398. https://doi.org/10.1137/S0040585X97T988691

Vancouver

Linke YY. Asymptotic properties of one-step weighted m-estimators with applications to regression. Theory of Probability and its Applications. 2018 Jan 1;62(3):373-398. doi: 10.1137/S0040585X97T988691

Author

Linke, Yu Yu. / Asymptotic properties of one-step weighted m-estimators with applications to regression. In: Theory of Probability and its Applications. 2018 ; Vol. 62, No. 3. pp. 373-398.

BibTeX

@article{36d53e190d744d7a8d4795a420275caf,
title = "Asymptotic properties of one-step weighted m-estimators with applications to regression",
abstract = "We study the asymptotic behavior of one-step weighted M-estimators based on independent not necessarily identically distributed observations, which approximate consistent weighted M-estimators. We find sufficient conditions for asymptotic normality of these estimators. As an application, we consider some known regression models where the one-step estimation under consideration allows us to construct explicit asymptotically optimal estimators having the same accuracy as the least-squares or quasi-likelihood estimators.",
keywords = "Asymptotic normality, Initial estimator, M-estimators, Newton{\textquoteright}s iteration method, Nonlinear regression, One-step M-estimators, One-step weighted M-estimators",
author = "Linke, {Yu Yu}",
year = "2018",
month = jan,
day = "1",
doi = "10.1137/S0040585X97T988691",
language = "English",
volume = "62",
pages = "373--398",
journal = "Theory of Probability and its Applications",
issn = "0040-585X",
publisher = "SIAM PUBLICATIONS",
number = "3",

}

RIS

TY - JOUR

T1 - Asymptotic properties of one-step weighted m-estimators with applications to regression

AU - Linke, Yu Yu

PY - 2018/1/1

Y1 - 2018/1/1

N2 - We study the asymptotic behavior of one-step weighted M-estimators based on independent not necessarily identically distributed observations, which approximate consistent weighted M-estimators. We find sufficient conditions for asymptotic normality of these estimators. As an application, we consider some known regression models where the one-step estimation under consideration allows us to construct explicit asymptotically optimal estimators having the same accuracy as the least-squares or quasi-likelihood estimators.

AB - We study the asymptotic behavior of one-step weighted M-estimators based on independent not necessarily identically distributed observations, which approximate consistent weighted M-estimators. We find sufficient conditions for asymptotic normality of these estimators. As an application, we consider some known regression models where the one-step estimation under consideration allows us to construct explicit asymptotically optimal estimators having the same accuracy as the least-squares or quasi-likelihood estimators.

KW - Asymptotic normality

KW - Initial estimator

KW - M-estimators

KW - Newton’s iteration method

KW - Nonlinear regression

KW - One-step M-estimators

KW - One-step weighted M-estimators

UR - http://www.scopus.com/inward/record.url?scp=85052738614&partnerID=8YFLogxK

U2 - 10.1137/S0040585X97T988691

DO - 10.1137/S0040585X97T988691

M3 - Article

AN - SCOPUS:85052738614

VL - 62

SP - 373

EP - 398

JO - Theory of Probability and its Applications

JF - Theory of Probability and its Applications

SN - 0040-585X

IS - 3

ER -

ID: 16336246