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http://dx.doi.org/10.5351/CKSS.2006.13.3.657

Minimum Variance Unbiased Estimation for the Maximum Entropy of the Transformed Inverse Gaussian Random Variable by Y=X-1/2  

Choi, Byung-Jin (Department of Applied Information Statistics, Kyonggi University)
Publication Information
Communications for Statistical Applications and Methods / v.13, no.3, 2006 , pp. 657-667 More about this Journal
Abstract
The concept of entropy, introduced in communication theory by Shannon (1948) as a measure of uncertainty, is of prime interest in information-theoretic statistics. This paper considers the minimum variance unbiased estimation for the maximum entropy of the transformed inverse Gaussian random variable by $Y=X^{-1/2}$. The properties of the derived UMVU estimator is investigated.
Keywords
Entropy; inverse Gaussian distribution; minimum variance unbiased estimator; asymptotic distribution;
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