Resumen de Point and interval estimation for the logistic distribution based on record data
Akbar Asgharzadeh, Reza Valiollahi, M. Abdi
In this paper, based on record data from the two-parameter lo gistic distribution, the maximum likelihood and Bayes estimators for the two unknown paramet ers are derived. The maximum like- lihood estimators and Bayes estimators can not be obtained i n explicit forms. We present a simple method of deriving explicit maximum likelihood estimators by approximating the likelihood func- tion. Also, an approximation based on the Gibbs sampling pro cedure is used to obtain the Bayes estimators. Asymptotic confidence intervals, bootstrap co nfidence 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 b een analysed for illustrative purposes
