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Asymptotic properties of one-step M-estimators

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  • Yuliana Linke

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.

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  • Yuliana Linke, 2019. "Asymptotic properties of one-step M-estimators," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(16), pages 4096-4118, August.
    • Handle: RePEc:taf:lstaxx:v:48:y:2019:i:16:p:4096-4118
    DOI: 10.1080/03610926.2018.1487982
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    Cited by:

    1. Yuliana Linke & Igor Borisov & Pavel Ruzankin & Vladimir Kutsenko & Elena Yarovaya & Svetlana Shalnova, 2022. "Universal Local Linear Kernel Estimators in Nonparametric Regression," Mathematics, MDPI, vol. 10(15), pages 1-28, July.

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