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10.1016/j.jksus.2020.10.021 ↗3. On predictive modeling using a new flexible Weibull distribution and machine learning approach: Analyzing the COVID-19 data
10.3390/math10111792 ↗4. The new novel discrete distribution with application on COVID-19 mortality numbers in kingdom of Saudi Arabia and Latvia
10.1155/2021/7192833 ↗9. A flexible extension of reduced Kies distribution: Properties, inference, and applications in biology
10.1155/2022/6078567 ↗12. Predictive modeling of the COVID-19 data using a new version of the flexible Weibull model and machine leaning techniques
10.3934/mbe.2023134 ↗14. Influence of COVID-19 vaccination on the dynamics of new infected cases in the world
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10.1155/2021/6915742 ↗16. An alternative one-parameter distribution for bounded data modeling generated from the lambert transformation
10.3390/sym13071190 ↗17. Power-modified Kies-exponential distribution: Properties, classical and Bayesian inference with an application to engineering data
10.3390/e24070883 ↗18. The power XLindley distribution: Statistical inference, fuzzy reliability, and COVID-19 application
10.1155/2022/9094078 ↗23. The Kumaraswamy distribution: Median-dispersion re-parameterizations for regression modeling and simulation-based estimation
10.1007/s00362-011-0417-y ↗26. The unit log–log distribution: A new unit distribution with alternative quantile regression modeling and educational measurements applications
10.1080/02664763.2021.2001442 ↗27. The unit-Weibull distribution as an alternative to the Kumaraswamy distribution for the modeling of quantiles conditional on covariates
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10.1155/2021/5461130 ↗