Introducing a New Alpha-power function distribution with estimation of parameters and application in health field

Abstract

In this research, we present an alternative statistical framework for analyzing cancer data, concentrating on the limits of existing lifetime failure models. By incorporating a New Alpha-Power Function Distribution (NAPFD) into the larger Alpha-Power Generated (APG) class, our approach outperforms standard models such as log-normal, Weibull, and generalized extreme value distributions in handling complex failure rate data patterns found in medical datasets. The NAPFD displays superior model fit and predicted accuracy in a careful comparison examination of five separate medical datasets, as evidenced by extensive goodness-of-fit criteria. Furthermore, we investigate the usefulness of numerous classical estimate strategies using simulation experiments, evaluating their performance using parameters such as absolute bias, mean square error, and mean relative error.

Keywords

• Generated Class
• Power Function Distribution
• Statistical Modeling
• Parameter Estimation Techniques
• Monte Carlo simulations