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http://dx.doi.org/10.7465/jkdi.2013.24.4.847

Goodness-of-fit test for normal distribution based on parametric and nonparametric entropy estimators  

Choi, Byungjin (Department of Applied Information Statistics, Kyonggi University)
Publication Information
Journal of the Korean Data and Information Science Society / v.24, no.4, 2013 , pp. 847-856 More about this Journal
Abstract
In this paper, we deal with testing goodness-of-fit for normal distribution based on parametric and nonparametric entropy estimators. The minimum variance unbiased estimator for the entropy of the normal distribution is derived as a parametric entropy estimator to be used for the construction of a test statistic. For a nonparametric entropy estimator of a data-generating distribution under the alternative hypothesis sample entropy and its modifications are used. The critical values of the proposed tests are estimated by Monte Carlo simulations and presented in a tabular form. The performance of the proposed tests under some selected alternatives are investigated by means of simulations. The results report that the proposed tests have better power than the previous entropy-based test by Vasicek (1976). In applications, the new tests are expected to be used as a competitive tool for testing normality.
Keywords
Entropy; entropy estimator; goodness-of-fit; normal distribution; power;
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Times Cited By KSCI : 1  (Citation Analysis)
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