The power Obulezi distribution with Monte Carlo simulation: Modeling teaching ratings, radiation, and lifetime data sets

In this current article, we introduce and propose a novel two parameters model known as power Obulezi distribution (POD), which consider mortality rate patterns and incorporates actuarial measures to better assess risk in statistical modeling. It is constructed by applying the power transformation techniques to the currently used Obulezi distribution. The newly introduced distribution enhances flexibility in the construction of statistical models, particularly when analyzing lifetime data, mortality trends, or risk-related phenomena using actuarial measures. Its statistical properties are carefully analyzed with an aim of testing whether it is possible to improve accuracy in data fitting and modeling. To check whether the POD is practical, we apply the maximum likelihood estimation on complete data. Extensive simulations will be performed to check the performance of those estimators. The POD is a powerful tool for modeling complex data in many fields. It works better than many other models when applied to teaching ratings, radiation and COVID-19 mortality rates datasets.