A new discrete generalized class of distribution with application to radiation and COVID-19 data

Developing new families of distributions is a prominent approach in statistical research. In statistical research, developing new families of distributions continues to rise in popularity. This article presents a new discrete generalized class of distribution, which is a flexible two-parameter discrete model based on the Reduced Kies distribution technique. A discrete generalized class of distribution is provided as a specific instance of the family. The relevance of the new distribution can be explained by its ability to model both surely and adversely skewed real data over a few generalized distributions with more than two parameters. Some properties of the new distribution are derived, Moments, order statistics, and some basic distributional properties of the new model are analysed. The MLE approach for parameter estimate is demonstrated, and the model employing an actual data set is also investigated. The model parameters are estimated using the MLE approach, and a comprehensive simulation study is employed for validation. The usefulness of the new model is demonstrated on a real data set. In addition, Monte Carlo simulation studies are used to relate the advantages of various estimating approaches. Finally, the utility of the new model is enhanced by the use of two real data sets to demonstrate the flexibility of the new distribution in modelling real-life data, including radiation and COVID-19 data, which underlines the real-world utility of the proposed model.