Empirical Bayes Test for Parameter of Inverse Exponential Distribution
Science Journal of Applied Mathematics and Statistics
Volume 4, Issue 5, October 2016, Pages: 236-241
Received: Sep. 4, 2016; Accepted: Sep. 12, 2016; Published: Oct. 8, 2016
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Guobing Fan, Department of Basic Subjects, Hunan University of Finance and Economics, Changsha, China
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The aim of this paper is to study the empirical Bayes test for the parameter of inverse exponential distribution. First, the Bayes test rule of one-sided test is derived in the case of independent and identically distributed random variables under weighted linear loss function. Then the empirical Bayes one-sided test rule is constructed by using the kernel-type density function and empirical distribution function. Finally, the asymptotically optimal property of the test function 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.
Empirical Bayes Test, Asymptotic Optimality, Convergence Rates, Weighted Linear Loss Function, Inverse Exponential Distribution
To cite this article
Guobing Fan, Empirical Bayes Test for Parameter of Inverse Exponential Distribution, Science Journal of Applied Mathematics and Statistics. Vol. 4, No. 5, 2016, pp. 236-241. doi: 10.11648/j.sjams.20160405.17
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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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