DISCRETIZATION OF THE METHOD OF GENERATING AN EXPANDED FAMILY OF DISTRIBUTIONS BASED UPON TRUNCATED DISTRIBUTIONS

Discretization translates the continuous functions into discrete version making them more adaptable for numerical computation and application in applied mathematics and computer sciences. In this article, discrete analogues of a generalization method of generating a new family of distributions is provided. Several new discrete distributions are derived using the proposed methodology. A discrete Weibull-Geometric distribution is considered and various of its significant characteristics including moment, survival function, reliability function, quantile function, and order statistics are discussed. The method of maximum likelihood and the method of moments are used to estimate the model parameters. The performance o f the proposed model is probed through a real data set. A comparison of our model with some existing models is also given to demonstrate its efficiency.

Automated summary

Discretization is an effective way to make continuous functions more suitable for numerical computation and application in various fields such as applied mathematics and computer sciences. This article presents a method for generating new distributions through discretization and discusses several new discrete distributions derived using this method. A discrete Weibull-Geometric distribution is analyzed in detail, including its characteristics and parameter estimation methods, with comparisons to existing models to demonstrate its efficiency.