Bayesian Estimation of Dynamic Cumulative Residual Entropy for Classical Pareto Distribution. Issue 1 (2nd January 2018)

• Record Type: Journal Article
• Authors: Renjini, K. R.; Abdul Sathar, E. I.; Rajesh, G.
• Is Part Of: American journal of mathematical and management sciences. Volume 37:Issue 1(2018)
• Page Start: 1
• Page End: 13
• Publication Date: 2018-01-02
• Subjects: Bayesian estimation -- Pareto distribution -- dynamic cumulative residual entropy -- prior/posterior distribution -- loss functions; Operations research -- Periodicals; Management science -- Periodicals; Periodicals; 658.4034
• DOI: 10.1080/01966324.2017.1364184
• Languages: English
• ISSNs: 0196-6324

Abstract

SYNOPTIC ABSTRACT: Dynamic cumulative residual entropy plays a significant role in reliability and survival analysis to model and analyze the data. This article presents Bayesian estimation of the dynamic cumulative residual entropy of the classical Pareto distribution using informative and non-informative priors. The Bayes estimators and their associated posterior risks are calculated under different symmetric and asymmetric loss functions. A numerical example is given to illustrate the results derived. Based on a Monte Carlo simulation study, comparisons are made between the proposed estimators. The objective of this article is to identify the combination of a loss function and a prior having the minimum Bayes risk in order to estimate efficiently the dynamic cumulative residual entropy of Pareto distribution.