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

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.

Quantile Estimation Using the Log-Skew-Normal Linear Regression Model with Application to Children’s Weight Data

“…Future research related to the LSNLRM will focus on additional diagnostic procedures, incomplete data and mixed models, as well as its multivariate extensions to model the relationship between covariates and quantiles of positive skewed response vectors. In addition, it is interesting to study the approach described in Mazucheli et al [2] for the LSN family, as well as for other distributions recently proposed in the statistical literature (MirMostafaee et al [27], Tamandi et al [28], Reyes and Iriarte [29]), including Bayesian and score-adjusted approaches to parameter estimation and inferential procedures (MirMostafaee et al [30], Ren and Hu [31], Nawa and Nadarajah [32]).…”