Some modified estimators for efficient estimation of power function model

The Power Function distribution is one of the most suitable distributions for failure times modeling, especially of electric components and devices. The article proposes some new modified estimators for parameter estimation of the Power Function distribution. The proposed modified estimators are based on the mean and geometric mean of the distribution. The performance of these estimators is compared with existing traditional and modified estimators by means of Monte Carlo simulation and two real-life examples. Total mean square error, total relative deviation, root mean square error and mean absolute error are used as performance criteria. The performance of the estimators is also compared on two real-life data sets repressing the device failures times. From both, Monte Carlo simulation and real-life applications, the results indicated that the proposed modified estimators perform better than the competing estimators and hence their use is recommended for parameter estimation of the Power Function probability model. The findings will help to get more precise parameter estimates for the distribution which is very much applied in different fields particularly in modelling failure times of electronic devices and components.