Goodness of Fit Tests based on Information Criterion for Randomly Censored Data

‎We propose two goodness of fit test statistics based on‎ Cumulative Kullback--Leibler (CKL) information and Cumulative residual Kullback--Leibler (CRKL) information for exponential distributions with unknown parameter and randomly censored data‎. ‎Koziol and Green introduced the Cramér-von Mises statistic with randomly censored data for a simple hypothesis based on the Kaplan--Meier product limit of the distribution function‎. ‎We use their idea to obtain test statistics based on CKL and CRKL for a randomly censored exponential distribution with estimated parameters‎.
‎The power of the proposed tests for testing exponentiality is compared with the test statistic based on the empirical distribution function using the opinion of Koziol and Green‎. ‎A simulation study is performed under a special censorship model introduced by Koziol and Green‎. ‎Simulation studies show a relatively high power of proposed test statistics in many alternatives‎.