Estimation of Entropy for Log-Logistic Distribution under Progressive Type II Censoring

Entropy is a useful indicator of information content that has been used in number applications. The Log-Logistic (LL) distribution probability often employed survival analysis. This paper addresses the problem estimating multiple entropy metrics for an LL using progressive type II censoring. We derive formulas six different types measurements. To obtain estimators proposed measures, maximum likelihood approach applied. Approximate confidence intervals are calculated under discussion. A numerical evaluation performed various censoring methods and sample sizes to characterize behavior estimator’s measures relative biases, related mean squared errors, average interval lengths, coverage probabilities. Numerical analysis revealed accuracy improve with size, suggested estimates genuine values as levels decrease. Finally, actual dataset was evaluated demonstration purposes.