Inferences for the Generalized Logistic Distribution Based on Record Statistics

作     者：Rashad M. El-Sagheer 

作者机构：Mathematics Department Faculty of Science Al-Azhar University Cairo Egypt 

出 版 物：《Intelligent Information Management》 (智能信息管理（英文）)

年 卷 期：2014年第6卷第4期

页      面：171-182页

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学] 

主　　题：Generalized Logistic Distribution (GLD) Record Statistics Parametric Bootstrap Methods Bayes Estimation Markov Chain Monte Carlo (MCMC) Gibbs and Metropolis Sampler 

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.