A Generalization of New Pareto-Type Distribution

The main purpose of data science is to uncover patterns in data using various statistical techniques. This article introduces a new heavy-tailed distribution, the Generalized New Pareto-Type (GNPT) distribution, as an alternative to the Pareto distribution. The GNPT distribution is shown to be more flexible and better fitting for real-world data, such as relief times of individuals receiving an analgesic, strength of carbon fibers, and monthly tax revenues. The article details the mathematical properties of the GNPT distribution, including its density function, hazard rate function, quantiles, and moments. It also discusses point estimation methods and compares the GNPT distribution with other competitive distributions through real data examples. The flexibility and superior fitting ability of the GNPT distribution make it a promising tool for modeling heavy-tailed data in various fields.