Mixture Modeling of Exponentiated Pareto Distribution in Bayesian Framework With Applications of Wind-Speed and Tensile Strength of Carbon Fiber

Abstract

Mixture modelling has stunning applications to explain the composite problems in simple way. Bayesian demonstration of 3-Component mixture model of Exponentiated Pareto distribution in right-type-I censoring scheme is presented in this article. The posterior densities of the parameter(s) are attained supposing the non-informative (uniform, Jeffreys) priors. The symmetric and asymmetric Loss Functions (Squared Error, Precautionary, Quadratic and DeGroot Loss Function) are assumed to get the Bayes estimator(s) and posterior risk(s). The presentation of the Bayes estimator(s) over posterior risk(s) in the studied loss functions is examined over simulation practice. Two real-data sets, wind speed and tensile strength of carbon fiber, are also analyzed for mixture to complete the performance of Bayes estimator(s). To enhance the study, the limiting forms are also derived for Bayes estimator(s) and posterior risk(s). The results reveal that for the component parameter(s), the Bayes Estimator(s

Reliability Analysis and Applications of Generalized Type-II Progressively Hybrid Maxwell–Boltzmann Censored Data

“…This application analyzes a meteorological data set reflecting the daily average wind speed (AWS) data from 1 January to 10 April 2009, in Cairo, Egypt. This data set was originally taken from Ghazal and Hasaballah [41] and later reanalyzed by Cheema et al [42]. For calculation convenience, each point in the AWS data is divided by ten.…”

Testing average wind speed using sampling plan for Weibull distribution under indeterminacy

“…The meteorologists are interested to see the daily average wind speed under indeterminacy. The average wind speed data of Cairo city is taken from 40 and shown in Table 7 . It is found that the wind speed data follows the Weibull distribution with shape parameter 2.7857 and scale parameter .…”