Transmuted New Weighted Weibull distribution: Some statistical properties and application to survival time data

Umar, Adamu Abubakar; Usman, Aliyu; Abubakar, Sani Salihu; Ieren, Terna Godfrey; Falgore, Jamilu Yunusa; Ishaq, Aliyu Ismail; Eraikhuemen, Innocent Boyle; Sulaiman, Bukar

doi:10.1063/5.0076107

Cited by 1 publication

(2 citation statements)

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“…The choice of a statistical distribution to describe a given dataset significantly influences the course of statistical analysis. In response to the increasing complexity and diversity of datasets, researchers are developing more robust statistical models that provide accurate and reliable predictions of underlying processes [1]. To enhance the versatility of the distribution in various real-world applications, researchers in distribution theory frequently introduce additional parameters to the underlying distribution for data modeling [2].…”

Section: Introductionmentioning

confidence: 99%

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The Generalized Odd Maxwell-Kumaraswamy Distribution: Its Properties and Applications

Ishaq,

Panitanarak,

Abiodun

et al. 2024

Contemp. Math.

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This study introduces a novel family of continuous probability distributions using Alzaatreh‘s technique to explore the Generalized Odd Maxwell-Generated (GOM-G) distribution family. Within the Generalized Odd Maxwell family, the GOM-Kumaraswamy distribution is presented as extended form of the Kumaraswamy distribution. The investigation thoroughly examines the cumulative distribution function, probability density function, hazard, and survival functions, as well as mixture representations of the Generalized Odd Maxwell-Kumaraswamy distribution. The suggested distribution's structural properties, including moments, skewness, kurtosis, probability-weighted moments, entropies, stress-strength models, and order statistics, are derived. Model parameters are estimated through the maximum likelihood method, and simulation studies evaluate the performance of maximum likelihood estimations using the quantile function. Employing two real-life datasets, goodness-of-fit measures such as Akaike Information Criterion (AIC), Corrected Akaike Information Criterion, Bayesian Information Criterion (BIC), Hannan-Quinn Information Criterion (HQIC), and chi-square goodness-of-fit tests demonstrate the adaptability and flexibility of the GOM-Kumaraswamy distribution against competing distributions, including Kumaraswamy-Kumaraswamy and Kumaraswamy-Burr III. The results reveal that the GOM-Kumaraswamy distribution exhibits the lowest AIC, CAIC, BIC, and HQIC values and the highest goodness-of-fit values compared to other models, suggesting its superiority as the preferred fit for both dataset forms. This proposed distribution contributes to practical applications, unveiling its potential significance in modeling real-world phenomena within the domains of hydrology and engineering.

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Section: Introductionmentioning

confidence: 99%

“…Many researchers have considered the Maxwell distribution as a suitable lifetime model and have applied it in various applications. For example, [18] obtained the reliability, Bayesian, and minimum variance unbiased estimators (1) (2)…”

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confidence: 99%