The location-scale family of generalized Maxwell distributions: theory and applications to annual rainfall records

Singh, Sudhanshu V.; Sharma, Vikas Kumar

doi:10.1007/s13198-019-00900-y

Cited by 2 publications

(3 citation statements)

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“…Let X ~ GOM − Kw(α, β, a, b) with pdf defined in (25), then the r th incomplete moments of X is given by…”

Section: Incomplete Momentsmentioning

confidence: 99%

“…and F(x 2 ; α, β, a, b = b 2 ) are presented as a mixture of exponentiated-G density defined respectively in (21) and (25). By taking the products of these functions we can have…”

Section: Stress-strength Modelmentioning

confidence: 99%

“…The physical properties of this distribution were derived, showing that the proposed distribution could be considered compared to other Weighted models. Singh and Sharma [25] added an extra parameter to the Maxwell distribution and defined the Location-scale family of generalized Maxwell distributions. The failure rate of this distribution could be increasing, and bathtub shaped.…”

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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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“…Let X ~ GOM − Kw(α, β, a, b) with pdf defined in (25), then the r th incomplete moments of X is given by…”

Section: Incomplete Momentsmentioning

confidence: 99%

“…and F(x 2 ; α, β, a, b = b 2 ) are presented as a mixture of exponentiated-G density defined respectively in (21) and (25). By taking the products of these functions we can have…”

Section: Stress-strength Modelmentioning

confidence: 99%

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confidence: 99%

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