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)
• Published : 2006.12.31

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.

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Cited by

1. A Modi ed Entropy-Based Goodness-of-Fit Tes for Inverse Gaussian Distribution vol.24, pp.2, 2011, https://doi.org/10.5351/KJAS.2011.24.2.383