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Inferences for the Generalized Logistic Distribution Based on Record Statistics
Mathematics Department, Faculty of Science, Al-Azhar University, Cairo, Egypt
- 1 Mathematics Department, Faculty of Science, Al-Azhar University, Cairo, Egypt
Intelligent Information Management·Volume 06 (2014)·Pages 171–182·Published 8 July 2014·DOI10.4236/iim.2014.64018
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Abstract
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.
KeywordsGeneralized Logistic Distribution (GLD)Record StatisticsParametric Bootstrap MethodsBayes EstimationMarkov Chain Monte Carlo (MCMC)Gibbs and Metropolis Sampler
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