Inferences for the Generalized Logistic Distribution Based on Record Statistics  

作　　者：Rashad M. El-Sagheer 

机构地区：[1]Mathematics Department, Faculty of Science, Al-Azhar University, Cairo, Egypt

出　　处：《Intelligent Information Management》2014年第4期171-182,共12页智能信息管理（英文）

摘　　要：Estimation for the parameters of the generalized logistic distribution (GLD) is obtained based on record statistics from a Bayesian and non-Bayesian approach. The Bayes estimators cannot be obtained in explicit forms. So the Markov chain Monte Carlo (MCMC) algorithms are used for computing the Bayes estimates. Point estimation and confidence intervals based on maximum likelihood and the parametric bootstrap methods are proposed for estimating the unknown parameters. A numerical example has been analyzed for illustrative purposes. Comparisons are made between Bayesian and maximum likelihood estimators via Monte Carlo simulation.Estimation for the parameters of the generalized logistic distribution (GLD) is obtained based on record statistics from a Bayesian and non-Bayesian approach. The Bayes estimators cannot be obtained in explicit forms. So the Markov chain Monte Carlo (MCMC) algorithms are used for computing the Bayes estimates. Point estimation and confidence intervals based on maximum likelihood and the parametric bootstrap methods are proposed for estimating the unknown parameters. A numerical example has been analyzed for illustrative purposes. Comparisons are made between Bayesian and maximum likelihood estimators via Monte Carlo simulation.

关 键 词：Generalized Logistic Distribution (GLD) RECORD Statistics Parametric BOOTSTRAP Methods BAYES Estimation Markov Chain Monte Carlo (MCMC) Gibbs and METROPOLIS SAMPLER 

分 类 号：O21[理学—概率论与数理统计]