Inferences for the Generalized Logistic Distribution Based on Record Statistics


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.

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El-Sagheer, R. (2014) Inferences for the Generalized Logistic Distribution Based on Record Statistics. Intelligent Information Management, 6, 171-182. doi: 10.4236/iim.2014.64018.

Conflicts of Interest

The authors declare no conflicts of interest.


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