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Constructing initial estimators in one-step estimation procedures of nonlinear regression

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  • Linke, Yu.Yu.
  • Borisov, I.S.

Abstract

We discuss an approach to construct explicitly calculable consistent estimators for parameters of some nonlinear regression models. The estimators of such a kind can be used as initial estimators in one-step estimation procedures for unknown parameters of these models.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:stapro:v:120:y:2017:i:c:p:87-94
    DOI: 10.1016/j.spl.2016.09.022
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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.
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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.
    2. Igor S. Borisov & Yuliana Yu. Linke & Pavel S. Ruzankin, 2021. "Universal weighted kernel-type estimators for some class of regression models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(2), pages 141-166, February.
    3. 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.

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