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.