Abstract
Most of the data arising in reliability can be modelled by Pareto distribution. In the present paper, we proposed the Bayes estimation of dynamic cumulative residual entropy for the classical Pareto distribution under upper record values. This measure performs important roles in reliability and survival analysis to model and analyse the data. A class of informative and non-informative priors has been assumed to derive the corresponding posterior distributions. The Bayes estimators (BEs) and the associated posterior risks have been calculated under different symmetric and asymmetric loss functions. We demonstrate the use of the proposed Bayesian estimation procedure with the average July temperatures data of Neuenburg, Switzerland, during the period 1864–1993. The performance of the BEs has been evaluated and compared under a comprehensive simulation study. The purpose is to find out the combination of a loss function and a prior having the minimum Bayes risk and hence producing the best results.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
