A new detection of flexible Weibull model with application

Abstract

The new family of distributions may provide a better fit to the data than existing families of distributions. This can lead to more accurate modeling and prediction. The new family of distributions may provide insights into the underlying mechanisms that generate the data. This can lead to improved understanding of the phenomenon being studied. The new family of distributions may generalize existing families, allowing for more flexible modeling and analysis. The new family of distributions may have applications in areas where existing families are not suitable or have not been explored. Overall, a new family of distributions can expand the toolkit available to researchers and practitioners, leading to improved modeling, analysis, and understanding in a variety of fields. In this paper, we introduce a new family of distributions, which we call the generalized Weibull exponential family. This family includes several well-known distributions as special cases. One advantage of the generalized Weibull exponential family is its flexibility in modeling a wide range of shapes and scales. It can handle both increasing and decreasing hazard rates, which are important in reliability analysis. Moreover, it can capture heavy-tailed or light-tailed behavior depending on the choice of parameters. In conclusion, we believe that the generalized Weibull exponential family is a useful addition to the existing families of distributions. Its flexibility and tractability make it a promising candidate for modeling data in various applications. Based on the proposed family, we introduce a new model with five parameters, named the generalized Weibull exponential distribution. Some mathematical properties were obtained. Maximum likelihood estimates for the model parameters were derived. A Monte Carlo simulation study was derived to assess the performance of the maximum likelihood estimates. A simulation study based on the parameters of the proposed model was performed. Finally, an application to real data set was performed.