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Asymptotic normality of one-step M-estimators based on non-identically distributed observations

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  • Linke, Yuliana Yu.

Abstract

We find general conditions for asymptotic normality of two types of one-step M-estimators based on independent not necessarily identically distributed observations. As an application, we consider some examples of one-step approximation of quasi-likelihood estimators in nonlinear regression.

Suggested Citation

  • Linke, Yuliana Yu., 2017. "Asymptotic normality of one-step M-estimators based on non-identically distributed observations," Statistics & Probability Letters, Elsevier, vol. 129(C), pages 216-221.
    • Handle: RePEc:eee:stapro:v:129:y:2017:i:c:p:216-221
    DOI: 10.1016/j.spl.2017.05.020
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    References listed on IDEAS

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    1. J. Fan & J. Chen, 1999. "One‐step local quasi‐likelihood estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(4), pages 927-943.
    2. Linke, Yu.Yu. & Borisov, I.S., 2017. "Constructing initial estimators in one-step estimation procedures of nonlinear regression," Statistics & Probability Letters, Elsevier, vol. 120(C), pages 87-94.
    3. Bergesio, Andrea & Yohai, Victor J., 2011. "Projection Estimators for Generalized Linear Models," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 661-671.
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