American Journal of Theoretical and Applied Statistics

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Empirical Bayes One-side Test for Inverse Exponential Model based on Negative Associate Samples

Received: 05 September 2016    Accepted: 23 September 2016    Published: 15 October 2016
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Abstract

By using the kernel-type density estimation and empirical distribution function in the case of identically distributed and negatively associated samples, the empirical Bayes one-sided test rules for the parameter of inverse exponential distribution are constructed based on negative associate sample under weighted linear loss function, and the asymptotically optimal property is obtained . It is shown that the convergence rates of the proposed empirical Bayes test rules can arbitrarily close to O(n-1/2) under suitable conditions.

DOI 10.11648/j.ajtas.20160506.12
Published in American Journal of Theoretical and Applied Statistics (Volume 5, Issue 6, November 2016)
Page(s) 342-347
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 test, Negatively Associated Sample, Asymptotic Optimality, Weighted Linear Loss Function, Inverse Exponential Distribution

References
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[9] Qian Z., Wei L., 2013. The two-sided empirical Bayes test of parameters for scale exponential family under weighed loss function. Journal of University of Science & Technology of China, 2(2): 156-161.
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Author Information
  • Department of Basic Subjects, Hunan University of Finance and Economics, Changsha, China

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    Guobing Fan. (2016). Empirical Bayes One-side Test for Inverse Exponential Model based on Negative Associate Samples. American Journal of Theoretical and Applied Statistics, 5(6), 342-347. https://doi.org/10.11648/j.ajtas.20160506.12

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    ACS Style

    Guobing Fan. Empirical Bayes One-side Test for Inverse Exponential Model based on Negative Associate Samples. Am. J. Theor. Appl. Stat. 2016, 5(6), 342-347. doi: 10.11648/j.ajtas.20160506.12

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    AMA Style

    Guobing Fan. Empirical Bayes One-side Test for Inverse Exponential Model based on Negative Associate Samples. Am J Theor Appl Stat. 2016;5(6):342-347. doi: 10.11648/j.ajtas.20160506.12

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  • @article{10.11648/j.ajtas.20160506.12,
      author = {Guobing Fan},
      title = {Empirical Bayes One-side Test for Inverse Exponential Model based on Negative Associate Samples},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {5},
      number = {6},
      pages = {342-347},
      doi = {10.11648/j.ajtas.20160506.12},
      url = {https://doi.org/10.11648/j.ajtas.20160506.12},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajtas.20160506.12},
      abstract = {By using the kernel-type density estimation and empirical distribution function in the case of identically distributed and negatively associated samples, the empirical Bayes one-sided test rules for the parameter of inverse exponential distribution are constructed based on negative associate sample under weighted linear loss function, and the asymptotically optimal property is obtained . It is shown that the convergence rates of the proposed empirical Bayes test rules can arbitrarily close to O(n-1/2) under suitable conditions.},
     year = {2016}
    }
    

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    T1  - Empirical Bayes One-side Test for Inverse Exponential Model based on Negative Associate Samples
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    AB  - By using the kernel-type density estimation and empirical distribution function in the case of identically distributed and negatively associated samples, the empirical Bayes one-sided test rules for the parameter of inverse exponential distribution are constructed based on negative associate sample under weighted linear loss function, and the asymptotically optimal property is obtained . It is shown that the convergence rates of the proposed empirical Bayes test rules can arbitrarily close to O(n-1/2) under suitable conditions.
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