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Article

Estimation for Entropy and Parameters of Generalized Bilal Distribution under Adaptive Type II Progressive Hybrid Censoring Scheme

by 1, 2,* and 2
1
School of Electronics Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
2
School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an 710072, China
*
Author to whom correspondence should be addressed.
Entropy 2021, 23(2), 206; https://doi.org/10.3390/e23020206
Received: 17 December 2020 / Revised: 31 January 2021 / Accepted: 4 February 2021 / Published: 8 February 2021
(This article belongs to the Special Issue The Statistical Foundations of Entropy)
Entropy measures the uncertainty associated with a random variable. It has important applications in cybernetics, probability theory, astrophysics, life sciences and other fields. Recently, many authors focused on the estimation of entropy with different life distributions. However, the estimation of entropy for the generalized Bilal (GB) distribution has not yet been involved. In this paper, we consider the estimation of the entropy and the parameters with GB distribution based on adaptive Type-II progressive hybrid censored data. Maximum likelihood estimation of the entropy and the parameters are obtained using the Newton–Raphson iteration method. Bayesian estimations under different loss functions are provided with the help of Lindley’s approximation. The approximate confidence interval and the Bayesian credible interval of the parameters and entropy are obtained by using the delta and Markov chain Monte Carlo (MCMC) methods, respectively. Monte Carlo simulation studies are carried out to observe the performances of the different point and interval estimations. Finally, a real data set has been analyzed for illustrative purposes. View Full-Text
Keywords: entropy; generalized Bilal distribution; adaptive Type-II progressive hybrid censoring scheme; maximum likelihood estimation; Bayesian estimation; Lindley’s approximation; confidence interval; Markov chain Monte Carlo method entropy; generalized Bilal distribution; adaptive Type-II progressive hybrid censoring scheme; maximum likelihood estimation; Bayesian estimation; Lindley’s approximation; confidence interval; Markov chain Monte Carlo method
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MDPI and ACS Style

Shi, X.; Shi, Y.; Zhou, K. Estimation for Entropy and Parameters of Generalized Bilal Distribution under Adaptive Type II Progressive Hybrid Censoring Scheme. Entropy 2021, 23, 206. https://doi.org/10.3390/e23020206

AMA Style

Shi X, Shi Y, Zhou K. Estimation for Entropy and Parameters of Generalized Bilal Distribution under Adaptive Type II Progressive Hybrid Censoring Scheme. Entropy. 2021; 23(2):206. https://doi.org/10.3390/e23020206

Chicago/Turabian Style

Shi, Xiaolin, Yimin Shi, and Kuang Zhou. 2021. "Estimation for Entropy and Parameters of Generalized Bilal Distribution under Adaptive Type II Progressive Hybrid Censoring Scheme" Entropy 23, no. 2: 206. https://doi.org/10.3390/e23020206

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