The Weibull Claim Model: Bivariate Extension, Bayesian, and Maximum Likelihood Estimations. (4th May 2022)

• Record Type: Journal Article
• Authors: Emam, Walid; Tashkandy, Yusra
• Other Names: Gómez-Déniz Emilio Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2022(2022)
• Publication Date: 2022-05-04
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2022/8729529
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : Using a class of claim distributions, we introduce the Weibull claim distribution, which is a new extension of the Weibull distribution with three parameters. The maximum likelihood estimation method is used to estimate the three unknown parameters, and the asymptotic confidence intervals and bootstrap confidence intervals are constructed. In addition, we obtained the Bayesian estimates of the unknown parameters of the Weibull claim distribution under the squared error and linear exponential function (LINEX) and the general entropy loss function. Since the Bayes estimators cannot be obtained in closed form, we compute the approximate Bayes estimates via the Markov Chain Monte Carlo (MCMC) procedure. By analyzing the two data sets, the applicability and capabilities of the Weibull claim model are illustrated. The fatigue life of a particular type of Kevlar epoxy strand subjected to a fixed continuous load at a pressure level of 90% until the strand fails data set was analyzed.