Using Approximate MLE for Testing Normality Based on Kullback-Leibler Information with Progressively Type-II Censored Data

TL;DRAbstract

We will use the joint entropy of progressively censored order statistics in terms of an incomplete integral of the hazard function, and provide a simple estimate of the joint entropy of progressively Type-II censored data, has been introduced by Balakrishnan et al. (2007). Then We construct a goodness-of-fit test statistic based on KullbackLeibler information for Normal distribution by using approximate MLE. Finally, we used Monte Carlo simulations, the power of the test is estimated and compared against several alternatives under different progressive censoring schemes.

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