A New Extension of the Kumaraswamy Generated Family of Distributions with Applications to Real Data

In this paper, we develop the new extended Kumaraswamy generated (NEKwG) family of distributions. It aims to improve modeling capability standard by using a one-parameter exponential-logarithmic transformation. Mathematical developments NEKwG are provided, such as probability density function series representation, moments, information measure, and order statistics, along with asymptotic distribution results. Two special distributions highlighted discussed, namely, uniform (NEKwU) exponential (NEKwE) They differ in support, but both have features generate models that accommodate versatile skewed data non-monotone failure rates. We employ maximum likelihood, least-squares estimation, Bayes estimation methods for parameter estimation. The performance these is discussed simulation studies. Finally, two real applications used show flexibility importance NEKwU NEKwE practice.