A new modified cubic transmuted-G family of distributions: Properties and different methods of estimation with applications to real-life data

Generating new statistical models is one of the most commonly used methods to represent real-life occurrences. Sometimes, with a known distribution, it may not be possible to represent a scenario, so we may consider generalization to incorporate more variance in the data. In this article, we offer a modified cubic transmuted-G family of distributions to enrich the members of the second-order transmuted distributions. By utilizing this modified class of distributions, we build a new distribution entitled the modified cubic transmuted Rayleigh distribution, along with its distributional features, estimate, associated inference, and real-life applications.