Bayesian and Classical Inference for Generalized Stress-Strength Parameter Under Generalized Logistic Distribution
نویسندگان
چکیده
In this paper, we study generalized stress-strength model for logistic distribution. The maximum likelihood estimator of quantity is obtained and then a confidence interval presented it. Bayesian bootstrap methods are also applied the recommended model. A Markov Chain Monte Carlo (MCMC) simulation assessing estimation performed via Metropolis-Hastings algorithm in each step Gibbs algorithm. An application to real data set addressed.
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ژورنال
عنوان ژورنال: Statistics, Optimization and Information Computing
سال: 2021
ISSN: ['2310-5070', '2311-004X']
DOI: https://doi.org/10.19139/soic-2310-5070-1292