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http://dx.doi.org/10.5351/KJAS.2011.24.2.383

A Modi ed Entropy-Based Goodness-of-Fit Tes for Inverse Gaussian Distribution  

Choi, Byung-Jin (Department of Applied Information Statistics, Kyonggi University)
Publication Information
The Korean Journal of Applied Statistics / v.24, no.2, 2011 , pp. 383-391 More about this Journal
Abstract
This paper presents a modified entropy-based test of fit for the inverse Gaussian distribution. The test is based on the entropy difference of the unknown data-generating distribution and the inverse Gaussian distribution. The entropy difference estimator used as the test statistic is obtained by employing Vasicek's sample entropy as an entropy estimator for the data-generating distribution and the uniformly minimum variance unbiased estimator as an entropy estimator for the inverse Gaussian distribution. The critical values of the test statistic empirically determined are provided in a tabular form. Monte Carlo simulations are performed to compare the proposed test with the previous entropy-based test in terms of power.
Keywords
Inverse Gaussian distribution; entropy; entropy characterization; entropy estimator; entropy-based test; power;
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Times Cited By KSCI : 1  (Citation Analysis)
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