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http://dx.doi.org/10.5351/KJAS.2013.26.4.611

A Test of Fit for Inverse Gaussian Distribution Based on the Probability Integration Transformation  

Choi, Byungjin (Department of Applied Information Statistics, Kyonggi University)
Publication Information
The Korean Journal of Applied Statistics / v.26, no.4, 2013 , pp. 611-622 More about this Journal
Abstract
Mudholkar and Tian (2002) proposed an entropy-based test of fit for the inverse Gaussian distribution; however, the test can be applied to only the composite hypothesis of the inverse Gaussian distribution with an unknown location parameter. In this paper, we propose an entropy-based goodness-of-fit test for an inverse Gaussian distribution that can be applied to the composite hypothesis of the inverse Gaussian distribution as well as the simple hypothesis of the inverse Gaussian distribution with a specified location parameter. The proposed test is based on the probability integration transformation. The critical values of the test statistic estimated by simulations are presented in a tabular form. A simulation study is performed to compare the proposed test under some selected alternatives with Mudholkar and Tian (2002)'s test in terms of power. The results show that the proposed test has better power than the previous entropy-based test.
Keywords
Inverse Gaussian distribution; entropy; probability integration transformation; goodness-of-fit; power;
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