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Asymptotic Optimal Empirical Bayes Estimation of the Parameter of ЭРланга Distribution

Received: 6 October 2016    Accepted: 14 October 2016    Published: 7 November 2016
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Abstract

This paper aims to study the empirical Bayes estimation of the parameter of ЭРланга distribution under a weighted squared error loss function. Bayes estimator is firstly to derive based on pivot method. Then empirical Bayes estimator of unknown parameter is constructed in a priori unknown circumstances. The asymptotically optimal property of this empirical Bayes estimator is also discussed. It is shown that the convergence rates of the proposed empirical Bayes estimator can arbitrarily close to O(n) -1) under suitable conditions.

Published in American Journal of Applied Mathematics (Volume 4, Issue 6)
DOI 10.11648/j.ajam.20160406.14
Page(s) 283-288
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Empirical Bayes Estimator, Asymptotic Optimality, Weighted Squared Error Loss Function, ЭРланга Distribution

References
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Cite This Article
  • APA Style

    Guobing Fan. (2016). Asymptotic Optimal Empirical Bayes Estimation of the Parameter of ЭРланга Distribution. American Journal of Applied Mathematics, 4(6), 283-288. https://doi.org/10.11648/j.ajam.20160406.14

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    ACS Style

    Guobing Fan. Asymptotic Optimal Empirical Bayes Estimation of the Parameter of ЭРланга Distribution. Am. J. Appl. Math. 2016, 4(6), 283-288. doi: 10.11648/j.ajam.20160406.14

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    AMA Style

    Guobing Fan. Asymptotic Optimal Empirical Bayes Estimation of the Parameter of ЭРланга Distribution. Am J Appl Math. 2016;4(6):283-288. doi: 10.11648/j.ajam.20160406.14

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  • @article{10.11648/j.ajam.20160406.14,
      author = {Guobing Fan},
      title = {Asymptotic Optimal Empirical Bayes Estimation of the Parameter of ЭРланга Distribution},
      journal = {American Journal of Applied Mathematics},
      volume = {4},
      number = {6},
      pages = {283-288},
      doi = {10.11648/j.ajam.20160406.14},
      url = {https://doi.org/10.11648/j.ajam.20160406.14},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajam.20160406.14},
      abstract = {This paper aims to study the empirical Bayes estimation of the parameter of ЭРланга distribution under a weighted squared error loss function. Bayes estimator is firstly to derive based on pivot method. Then empirical Bayes estimator of unknown parameter is constructed in a priori unknown circumstances. The asymptotically optimal property of this empirical Bayes estimator is also discussed. It is shown that the convergence rates of the proposed empirical Bayes estimator can arbitrarily close to O(n) -1) under suitable conditions.},
     year = {2016}
    }
    

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    T1  - Asymptotic Optimal Empirical Bayes Estimation of the Parameter of ЭРланга Distribution
    AU  - Guobing Fan
    Y1  - 2016/11/07
    PY  - 2016
    N1  - https://doi.org/10.11648/j.ajam.20160406.14
    DO  - 10.11648/j.ajam.20160406.14
    T2  - American Journal of Applied Mathematics
    JF  - American Journal of Applied Mathematics
    JO  - American Journal of Applied Mathematics
    SP  - 283
    EP  - 288
    PB  - Science Publishing Group
    SN  - 2330-006X
    UR  - https://doi.org/10.11648/j.ajam.20160406.14
    AB  - This paper aims to study the empirical Bayes estimation of the parameter of ЭРланга distribution under a weighted squared error loss function. Bayes estimator is firstly to derive based on pivot method. Then empirical Bayes estimator of unknown parameter is constructed in a priori unknown circumstances. The asymptotically optimal property of this empirical Bayes estimator is also discussed. It is shown that the convergence rates of the proposed empirical Bayes estimator can arbitrarily close to O(n) -1) under suitable conditions.
    VL  - 4
    IS  - 6
    ER  - 

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Author Information
  • Department of Basic Subjects, Hunan University of Finance and Economics, Changsha, China

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