Goodness of Fit Using a New Estimate of Kullback-Leibler Information Based on Type II Censored Data

Abstract

In this article, a general goodness of fit test is developed by using a new estimate of Kullback-Leibler (KL) information based on Type-II censored data. The proposed test is consistent, and the test statistic is nonnegative, just like KL information. Then, the test statistic is used to test for exponentiality based on Type-II censored data. Through a simulation study, power values of the proposed test are compared with some prominent existing tests. A real-life data analysis is finally presented for illustrative purpose.

Dynamic Robust Spectrum Sensing Based on Goodness-of-Fit Test Using Bilateral Hypotheses

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Assessing Model Validity Using KL Divergence and Beta-Stacy Process for Right Censored Data

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