A Novel Entropy-Transformed Inverse Weibull Distribution: Development, Properties, and Application in Diverse Data Modeling

Standard distributions must be improved to enhance their capacity for data modeling because they do not inherently suit all sorts of data sets in an acceptable manner. Due to this lack of previous ones, we developed a novel model employing the entropy-transformed function. We utilized the inverse Weibull model to function as the reference model to assess the applicability of the entropy transformation. The distribution, referred to as the “Entropy-Transformed Inverse Weibull Distribution” (ETIWL), is derived by applying the entropy transformation to the inverse Weibull model. The proposed distribution's core characteristics have been taken into account. The maximum-likelihood approach is used to estimate the parameters of the given distribution. Four real data sets are used in this study with the thorough simulation analysis to see whether the proposed distribution is superior.