Research Article | OPEN ACCESS
Empirical Bayes Estimation for Exponential Model Using Non-parameter Polynomial Density Estimator
1Manfeng Liu and 2Haiping Ren
1Cluster and Enterprise Development Research Center
2School of Information Management, Jiangxi University of Finance and Economics, China
Research Journal of Applied Sciences, Engineering and Technology 2013 2:392-397
Received: April 29, 2012 | Accepted: May 23, 2012 | Published: January 11, 2013
Abstract
In this study, we study the empirical Bayes estimation of the parameter of the exponential distribution. In the empirical Bayes procedure, we employ the non-parameter polynomial density estimator to the estimation of the unknown marginal probability density function, instead of estimating the unknown prior probability density function of the parameter. Empirical Bayes estimators are derived for the parameter of the exponential distribution under squared error and LINEX loss functions. We use numerical examples to compare the empirical Bayes estimators we obtained under squared error and LINEX loss functions and we get the result of the mean square error of the empirical Bayes estimator under LINEX loss is usually smaller than the estimator under squared error loss function, so it is more better.
Keywords:
Empirical bayes estimator, LINEX loss function, non-parameter polynomial density estimator, squared error loss,
Competing interests
The authors have no competing interests.
Open Access Policy
This article is 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, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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