A New Generated Family of Distributions: Statistical Properties and Applications with Real-Life Data
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Date
2023
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Wiley / Hindawi
Abstract
Several standard distributions can be used to model lifetime data. Nevertheless, a number of these datasets from diverse fields such
as engineering, finance, the environment, biological sciences, and others may not fit the standard distributions. As a result, there is
a need to develop new distributions that incorporate a high degree of skewness and kurtosis while improving the degree of
goodness-of-fit in empirical distributions. In this study, by applying the T-X method, we proposed a new flexible generated
family, the Ramos-Louzada Generator (RL-G) with some relevant statistical properties such as quantile function, raw
moments, incomplete moments, measures of inequality, entropy, mean and median deviations, and the reliability parameter.
The RL-G family has the ability to model “right,” “left,” and “symmetric” data as well as different shapes of the hazard
function. The maximum likelihood estimation (MLE) method has been used to estimate the parameters of the RL-G. The
asymptotic performance of the MLE is assessed by simulation analysis. Finally, the flexibility of the RL-G family is
demonstrated through the application of three real complete datasets from rainfall, breaking stress of carbon fibers, and
survival times of hypertension patients, and it is evident that the RL-Weibull, which is a special case of the RL-G family,
outperformed its submodels and other distributions
Description
This article is published by Wiley / Hindawi 2023 and is also available at https://doi.org/10.1155/2023/9325679
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Citation
Hindawi Computational and Mathematical Methods Volume 2023, Article ID 9325679, 18 pages