Analysis of uncertainty measure using unified hybrid censored data with applications
Baria A. Helmy, Amal S. Hassan, Ahmed K. El-Kholy
Abstract
Entropy is a measure of random variable uncertainty that reflects the anticipated quantity of information. In this paper, estimation of Shannon entropy for Lomax distribution, viz unified hybrid censored data are considered. The processes of maximum likelihood and Bayesian estimation procedures are regarded. The Bayesian estimator under balanced squared error, balanced linear exponential, and balanced general entropy loss functions are derived. The purpose of a Monte Carlo simulation study is designed to compare the accuracy of different estimators in the context of specific measures. Real data analysis is employed to confirm the proposed estimators. In summary, we discovered that, according to the findings of the study, the mean squared errors values decrease as the sample size increases. In the majority of cases, Bayesian estimates under balanced linear exponential loss function are more appropriate in terms of simulation out comes than other specified loss functions.