Classical and Bayesian Estimation for the Exponentiated Obulezi Distribution: Its Applications in Engineering

In this study, we suggest a new distribution called the exponentiated Obulezi distribution (EOD). We propose this approach to promote flexibility with a parsimonious structure. Explicit closed forms of the mean, variance, $r^{th}$ moment, survival function, and hazard rate function of the proposed model are derived. The model parameters are estimated by several different methods like maximum likelihood, Least Squares estimation, Weighted least Squares estimation, Cramer-von Mises estimation, Anderson-Darling estimation, right-tailed Anderson-Darling estimation, and Bayesian estimation techniques. A detailed comparison of several estimation methodologies is carried out using simulation and real-life data application to study the performance of the proposed model. The simulation results indicate the consistency of the various parameter estimators ,while the real-life applications results reveal that the newly proposed model gives more accurate and consistent estimates compared to other competing models. In addition, we have demonstrated that our model is a better fit for an increasing hazard rate data set.