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Teor. Veroyatnost. i Primenen., 2015, Volume 60, Issue 1, Pages 80–98 (Mi tvp4606)  

This article is cited in 5 scientific papers (total in 5 papers)

Refinement of Fishers one-step estimators in the case of slowly converging preliminary estimators

Yu. Yu. Linkeab

a Novosibirsk State University
b Sobolev Institute of Mathematics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk

Funding Agency Grant Number
Russian Foundation for Basic Research 14-01-00220


DOI: https://doi.org/10.4213/tvp4606

Full text: PDF file (204 kB)
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English version:
Theory of Probability and its Applications, 2016, 60:1, 88–102

Bibliographic databases:

Received: 03.03.2014

Citation: Yu. Yu. Linke, “Refinement of Fishers one-step estimators in the case of slowly converging preliminary estimators”, Teor. Veroyatnost. i Primenen., 60:1 (2015), 80–98; Theory Probab. Appl., 60:1 (2016), 88–102

Citation in format AMSBIB
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    Citing articles on Google Scholar: Russian citations, English citations
    Related articles on Google Scholar: Russian articles, English articles

    This publication is cited in the following articles:
    1. Yu. Yu. Linke, “Asymptotic properties of one-step weighted $M$-estimators with application to some regression problems.”, Theory Probab. Appl., 62:3 (2018), 373–398  mathnet  crossref  crossref  mathscinet  zmath  isi  elib
    2. Yu. Yu. Linke, “Asymptotic normality of one-step $M$-estimators based on non-identically distributed observations”, Stat. Probab. Lett., 129 (2017), 216–221  crossref  mathscinet  zmath  isi
    3. Yu. A. Kutoyants, “On the multi-step MLE-process for ergodic diffusion”, Stoch. Process. Their Appl., 127:7 (2017), 2243–2261  crossref  mathscinet  zmath  isi
    4. Yu. Yu. Linke, I. S. Borisov, “Constructing explicit estimators in nonlinear regression problems”, Theory Probab. Appl., 63:1 (2018), 22–44  mathnet  crossref  crossref  isi  elib
    5. Linke Yu., “Asymptotic Properties of One-Step M-Estimators”, Commun. Stat.-Theory Methods, 48:16 (2019), 4096–4118  crossref  isi
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