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In recent years, information entropy has been studied and developed rapidly across disciplines as a measure of information value. In this article, the maximum likelihood estimation and EM algorithm are used to estimate the parameters of the log-logistic distribution for progressive type-I interval censored data, and the hypothesis testing algorithm of information entropy is proposed. Finally, Monte Carlo numerical simulations are conducted to justify the feasibility of the algorithm.

Statistical Inference for the Information Entropy of the Log-Logistic Distribution under Progressive Type-I Interval Censoring Schemes