The entropy-transformed Gompertz distribution: Distributional insights and cross-disciplinary utilizations
Tabassum Naz Sindhu, Anum Shafiq, Showkat Ahmad Lone, Tahani A. Abushal
Abstract
A novel two-parameter continuous model titled the entropy-transformed Gompertz (ETGPZ) distribution has been developed via the entropy transform. A new framework has been investigated and found to meet the criteria of the probability function. By significantly improving the functional shape and having the ability to model the most likely form of the hazard rate function, this new modification has increased the adaptability of the typical distribution. Some of its core characteristics, such as its statistical and computational features, are clearly presented. A thorough simulation analysis has been done to examine the final behavior of maximum likelihood estimators while estimating model parameters. We assess the performance and practical applicability of the ETGPZ distribution using eight real datasets from engineering and biomedical fields. The results demonstrate that the ETGPZ outperforms the baseline Gompertz (GPZ) distribution, highlighting its superiority and broader potential for various applications. • 2-parameter entropy-transformed Gompertz model with closed-form functions. • Validated model framework meets essential probability function requirements. • Enhanced shape flexibility for adaptable hazard rate function modeling. • Clear statistical features, avoiding complex mathematical expressions. • Simulations confirm MLE accuracy in parameter estimation.