Article HistoryStudy habits contribute significantly in the development of knowledge and perceptual capacities. Study habits tell a person that how much he will learn and how far he wants to go, and how much he wants to earn.
In this paper, Frechet distribution under Bayesian paradigm is studied. Posterior distributions are derived by using Gumbel Type-II and Levy prior. Quadrature numerical integration technique is utilized to solve posterior distribution. Bayes estimators and their risks have been obtained by using four loss functions. Prior predictive distributions are derived for elicitation of hyperparameters. The performance of Bayes estimators are compared by using Monte Carlo simulation study.
Scale parameter of Log logistic distribution has been studied using Bayesian approach. Posterior distribution has derived by using non informative prior. Posterior distribution is not in close form so we have work with quadrature numerical integration. Various loss functions has been utilized to derive the Bayes estimators and their corresponding risks. Simulation study has been performed to compare the performance of different estimators.
In this study, Frechet distribution has been studied by using Bayesian analysis. Posterior distribution has been derived by using gamma and exponential. Bayes estimators and their posterior risks has been derived using five different loss functions. Elicitation of hyperparameters has been done by using prior predictive distributions. Simulation study is carried out to study the behavior of posterior distribution. Quasi quadratic loss function and exponential prior are found better among all.
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