In this paper a new method of parameter estimation was employed for extended Burr XII parameters using the principle of maximum entropy (POME) based on k-record values. The Monte Carlo simulation was applied to assess the performance of this method and compare it with some other well-known methods. The simulated results showed that POME performs better than the other methods. تفاصيل المقالة

Characterizing distributions is the process of finding the unique properties that distinguish a particular distribution from others. There are various methods for doing this, such as using moments, maximum entropy, hazard rate, and so on. Nanda (2010) used failure rate and mean residual life functions to characterizing some distributions. In this paper, we extend the results of Nanda (2010). We characterize the Pareto, exponential, and Rayleigh distributions by refining the main results of that paper. We take some inequalities from that paper and propose a new inequality. Then, we raise both sides of the inequalities to some power and show that equality holds if and only if certain distributions are characterized. تفاصيل المقالة

Our aim at this paper is to estimate the parameters of extended Burr XII (EBXII) based on krecords with corrected bias. To get this goal maximum likelihood estimator (MLE) is applied and bias correction for ML estimation is considered to get better and improved estimator. Numerical results for comparison of performances of estimators are also presented. تفاصيل المقالة