A New Transmuted Generalized Lomax Distribution: Properties and Applications to COVID-19 Data.
Comput Intell Neurosci
; 2021: 5918511, 2021.
Article
in English
| MEDLINE | ID: covidwho-1463058
ABSTRACT
A new five-parameter transmuted generalization of the Lomax distribution (TGL) is introduced in this study which is more flexible than current distributions and has become the latest distribution theory trend. Transmuted generalization of Lomax distribution is the name given to the new model. This model includes some previously unknown distributions. The proposed distribution's structural features, closed forms for an rth moment and incomplete moments, quantile, and Rényi entropy, among other things, are deduced. Maximum likelihood estimate based on complete and Type-II censored data is used to derive the new distribution's parameter estimators. The percentile bootstrap and bootstrap-t confidence intervals for unknown parameters are introduced. Monte Carlo simulation research is discussed in order to estimate the characteristics of the proposed distribution using point and interval estimation. Other competitive models are compared to a novel TGL. The utility of the new model is demonstrated using two COVID-19 real-world data sets from France and the United Kingdom.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Models, Statistical
/
COVID-19
Limits:
Humans
Language:
English
Journal:
Comput Intell Neurosci
Journal subject:
Medical Informatics
/
Neurology
Year:
2021
Document Type:
Article
Affiliation country:
2021
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