@Article{cmes.2023.028783,
AUTHOR = {Hassan Okasha, Mazen Nassar},
TITLE = {On a New Version of Weibull Model: Statistical Properties, Parameter Estimation and Applications},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {137},
YEAR = {2023},
NUMBER = {3},
PAGES = {2219--2241},
URL = {http://www.techscience.com/CMES/v137n3/53738},
ISSN = {1526-1506},
ABSTRACT = {In this paper, we introduce a new four-parameter version of the traditional Weibull distribution. It is able to
provide seven shapes of hazard rate, including constant, decreasing, increasing, unimodal, bathtub, unimodal then
bathtub, and bathtub then unimodal shapes. Some basic characteristics of the proposed model are studied, including
moments, entropies, mean deviations and order statistics, and its parameters are estimated using the maximum
likelihood approach. Based on the asymptotic properties of the estimators, the approximate confidence intervals
are also taken into consideration in addition to the point estimators. We examine the effectiveness of the maximum
likelihood estimators of the model’s parameters through simulation research. Based on the simulation findings, it
can be concluded that the provided estimators are consistent and that asymptotic normality is a good method to get
the interval estimates. Three actual data sets for COVID-19, engineering and blood cancer are used to empirically
demonstrate the new distribution’s usefulness in modeling real-world data. The analysis demonstrates the proposed
distribution’s ability in modeling many forms of data as opposed to some of its well-known sub-models, such as
alpha power Weibull distribution.},
DOI = {10.32604/cmes.2023.028783}
}