Bayesian Estimations of Shannon Entropy and Rényi Entropy of Inverse Weibull Distribution

Haiping Ren () and Xue Hu
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Haiping Ren: Department of Basic Subjects, Jiangxi University of Science and Technology, Nanchang 330013, China
Xue Hu: College of Science, Jiangxi University of Science and Technology, Ganzhou 341000, China

Mathematics, 2023, vol. 11, issue 11, 1-16

Abstract: In this paper, under the symmetric entropy and the scale squared error loss functions, we consider the maximum likelihood (ML) estimation and Bayesian estimation of the Shannon entropy and Rényi entropy of the two-parameter inverse Weibull distribution. In the ML estimation, the dichotomy is used to solve the likelihood equation. In addition, the approximation confidence interval is given by the Delta method. Because the form of estimation results is more complex in the Bayesian estimation, the Lindley approximation method is used to achieve the numerical calculation. Finally, Monte Carlo simulations and a real dataset are used to illustrate the results derived. By comparing the mean square error between the estimated value and the real value, it can be found that the performance of ML estimation of Shannon entropy is better than that of Bayesian estimation, and there is no significant difference between the performance of ML estimation of Rényi entropy and that of Bayesian estimation.

Keywords: inverse Weibull distribution; symmetric entropy loss function; Rényi entropy; Bayesian estimation; Lindley approximation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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