S.A. OSAGIE

This study focuses on the development of continuous lifetime distribution to model real life data sets. One approach to creating new distributions is the T-X (Tranformer- Transformed) method, which involves either adding a number of parameters to an existing distribution, raising a distribution to a power or combining existing distributions. In this study, the Paralogistic-Chen distribution is generated using the T-X (Transformer- Transformed) method of obtaining distributions. This involves a combination of the paralogistic and the Chen distributions. Some of the properties of the Paralogistic-Chen distribution are considered in this study and the application of the distribution will be considered to show how well the distribution fits the data and the Maximum Likelihood Estimation (MLE) is used to obtain the parameters of the distribution.

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.