Novel Type I Half Logistic Burr-Weibull Distribution: Application to COVID-19 Data

Area of Science:

• Statistics and Probability
• Mathematical Modeling

Background:

• The need for flexible and accurate continuous probability distributions in data analysis.
• Limitations of existing distributions in capturing complex data patterns.

Purpose of the Study:

• To introduce and analyze the novel type I half logistic Burr-Weibull distribution.
• To provide classical, non-classical, and Bayesian estimation methods for model parameters.
• To demonstrate the distribution's superiority and flexibility using real-world data.

Main Methods:

• Development of the type I half logistic Burr-Weibull distribution.
• Application of maximum likelihood and other estimation techniques.
• Bayesian estimation using Markov Chain Monte Carlo (MCMC) simulations for parameter assessment.

Main Results:

• The proposed distribution exhibits enhanced performance in fitting diverse datasets.
• Parameter estimation was successfully performed using multiple statistical approaches.
• Real COVID-19 data from Saudi Arabia and Italy validated the model's practical utility.

Conclusions:

• The type I half logistic Burr-Weibull distribution is a valuable addition to statistical modeling.
• The study confirms the model's effectiveness and adaptability for complex data analysis.
• The proposed estimation methods provide robust parameter inference.