Abstract
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Article Information:
Empirical Bayes Estimation for Exponential Model Using Non-parameter Polynomial Density Estimator
Manfeng Liu and Haiping Ren
Corresponding Author: Manfeng Liu
Submitted: April 29, 2012
Accepted: May 23, 2012
Published: January 11, 2013 |
Abstract:
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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.
Key words: Empirical bayes estimator, LINEX loss function, non-parameter polynomial density estimator, squared error loss, , ,
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Abstract
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Cite this Reference:
Manfeng Liu and Haiping Ren, . Empirical Bayes Estimation for Exponential Model Using Non-parameter Polynomial Density Estimator. Research Journal of Applied Sciences, Engineering and Technology, (02): 392-397.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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