Abstract
In this paper, based on record data from the two-parameter logistic distribution, the maximum likelihood and Bayes estimators for the two unknown parameters are derived. The maximum likelihood estimators and Bayes estimators can not be obtained in explicit forms. We present a simplemethod of deriving explicit maximum likelihood estimators by approximating the likelihood function. Also, an approximation based on the Gibbs sampling procedure is used to obtain the Bayes estimators. Asymptotic confidence intervals, bootstrap confidence intervals and credible intervals are also proposed. Monte Carlo simulations are performed to compare the performances of the different proposed methods. Finally, one real data set has been analysed for illustrative purposes.
Keywords
- Logistic distribution
- record data
- maximum likelihood estimator
- Bayes estimator
- Gibbs sampling
Rights
Copyright
From February 2013 articles are under a Creative Commons license: CC BY-NC-ND You must attribute the work in the manner specified by the author or licensor (but not in any way that suggests that they endorse you or your use of the work), you may not use the work for commercial purposes and you may not alter, transform, or build upon the work.
