A Neutrosophic Rayleigh Approach Based on DUS- Transformation for Analysing COVID-19 Incubation Periods

Abstract

The DUS transformation to the Rayleigh distribution introduces better modelling of real
world variations and enhances its ability to represent skewed or heavy-tailed data. To address 
ambiguity, inconsistency, and indeterminacy in data, In this study, we introduce an extension of 
the neutrosophic Rayleigh distribution, the DUS Transformed Neutrosophic Rayleigh (DUSNR) 
Distribution. Important statistical aspects of the DUSNR distribution, such as quantiles, moments, 
moment-generating functions, and order statistics, are determined under neutrosophic conditions. 
The performance of the maximum likelihood estimator is assessed by simulation, showing that its 
accuracy increases as sample sizes rise. Lastly, the findings of applying the suggested distribution 
to the COVID-19 incubation dataset are contrasted with those of the DUS transformed Rayleigh 
distribution and the neutrosophic Rayleigh distribution. 

DOI 10.5281/zenodo.18777803