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Amin, A. (2012) Bayesian and Non-Bayesian Estimation from Type I Generalized Logistic Distribution Based on Lower Record Values. Journal of Applied Sciences Research, 8, 118-226.

has been cited by the following article:

• TITLE: Inferences for the Generalized Logistic Distribution Based on Record Statistics

  AUTHORS: Rashad M. El-Sagheer

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

  JOURNAL NAME: Intelligent Information Management, Vol.6 No.4, July 8, 2014

  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.