DELIGHT CHUKWUEBUKA AMANDI

Lifetime data analysis plays a crucial role in various fields, ranging from engineering to epidemiology. In this study, we investigate the effectiveness of the Paralogistic-Weibull distribution in modeling lifetime data compared to other competing distributions such as the Weibull and Paralogistic distributions. Two datasets were analyzed: the daily number of COVID-19 infected persons in Nigeria and the survival times of patients with Head and Neck Cancer. We employed goodness-of-fit tests, including the Kolmogorov-Smirnov, Anderson-Darling, and Cramér-von Mises tests, along with discrepancy criteria such as the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), to evaluate the performance of the distributions. The results indicate that the ParalogisticWeibull distribution consistently outperforms the other distributions across both datasets, exhibiting higher p-values and lower discrepancy criteria values. Therefore, we conclude that the Paralogistic-Weibull distribution offers superior flexibility and accuracy in modeling lifetime data, providing valuable insights for practitioners and researchers in the field of lifetime data analysis.