bayesian estimation

As application of compound distributions in the study of longevity data, in this paper the compound distribution of inverse Weibull Geometric is investigated. The presence of parameters of scale, shape and failure rate in this distribution, their study in terms of estimation and hypothesis testing is of particular importance. However, since the phenomenon of censorship is also considered in the study of lifetime data, so the parameters under the type-II of censorship are estimated using the maximum likelihood and Bayesian estimation. In Bayesian estimation, the parameters under different loss functions are estimated based on appropriate prior distributions, then the symmetric confidence interval and HPD are presented by simulation and the estimators are compared using statistical criteria. Finally, the goodness of fit of model is presented by the real data.

In this study, we consider the statistical inference of multicomponent stress-strength reliability when stress and strength variables follow power Topp-Leone distributions. This system has independent and identically distributed strength components and each component is exposed to a common stress and is reliable if at least out of strength variables exceed the stress variable. The reliability of the system is estimated in view of classical and Bayesian in two cases. In the first case, we suppose that the first shape parameters are same and second shape parameters are different and in the second case, it is assumed that the common first shape parameter is known. Finally, a simulation study is performed to compare the performances of the estimators and one data set is presented for the application of methods.

In this paper, statistical inference is considered for k-component coherent systems, when the system lifetime data is progressively type-II censored. In these coherent systems, it is assumed that the system structure and system signature are known and the component lifetime distribution is exponential. Pivotal and Bayesian methods are introduced for point estimation of the component lifetime parameter, and these methods are compared with the maximum likelihood and the least squares methods existing in the literature. Pivotal confidence interval, Bayesian confidence interval and confidence interval based on the likelihood ratio test are computed. Using Monte Carlo simulations, different point and interval estimates are compared and it is observed that pivotal and Bayesian methods perform better than other existing estimation methods.

In this research article,we estimate the multicomponent stress–strength reliability of a system when strength and stress variates are drawn from an exponentiated Weibull distribution with different shape parameters and , and common scale parameter , respectively. The reliability is estimated using the best single observation percentile method and maximum liklihood method of estimation when samples drawn from strength and stress distributions. The reliability estimators are compared asymptotically. The small sample comparison of the reliability estimates is made through Monte Carlo simulation. Using real data sets we illustrate the procedure. Keywords: Stress–Strength, Reliability, Maximum likelihood estimation, Best single observation percentile estimation, Mean square error, Confidence intervals, Gompertz distribution.