Bayesian and Classical Inference for Generalized Stress-Strength Parameter Under Generalized Logistic Distribution
Abstract
In this paper, we study generalized stress-strength model for generalized logistic distribution. The maximum likelihood estimator of this quantity is obtained and then a confidence interval is presented for it. Bayesian and bootstrap methods are also applied for the recommended model. A Markov Chain Monte Carlo (MCMC) simulation study for assessing the estimation methods is performed via the Metropolis-Hastings algorithm in each step of Gibbs algorithm. An application to real data set is addressed.References
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