Estimation of entropy for inverse Weibull distribution under multiple censored data

Abstract

Entropy is a measure of uncertainty in a random variable which quantifies the expected value of the information contained in that random variable. This article estimates the Shannon entropy of the inverse Weibull distribution in case of multiple censored data. The maximum likelihood estimator and the approximate confidence interval are derived. Simulation studies are performed to investigate the performance of the estimates at different sample sizes. Real data are analysed for illustration purposes. In general, based on the outcomes of study we reveal that the mean square errors values decrease as the sample size increases. The maximum likelihood of entropy estimates approaches the true value as the censoring level decreases. The intervals of the entropy estimates appear to be narrow as the sample size increases with high probability.

Keywords

• shannon entropy
• multiple censored
• inverse weibull distribution
• maximum likelihood estimators