Empirical Bayes One-side Test for Inverse Exponential Model based on Negative Associate Samples
American Journal of Theoretical and Applied Statistics
Volume 5, Issue 6, November 2016, Pages: 342-347
Received: Sep. 5, 2016; Accepted: Sep. 23, 2016; Published: Oct. 15, 2016
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Author
Guobing Fan, Department of Basic Subjects, Hunan University of Finance and Economics, Changsha, China
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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.
Keywords
Empirical Bayes test, Negatively Associated Sample, Asymptotic Optimality, Weighted Linear Loss Function, Inverse Exponential Distribution
To cite this article
Guobing Fan, Empirical Bayes One-side Test for Inverse Exponential Model based on Negative Associate Samples, American Journal of Theoretical and Applied Statistics. Vol. 5, No. 6, 2016, pp. 342-347. doi: 10.11648/j.ajtas.20160506.12
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Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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