On the Properties and Applications of Transmuted Odd Generalized Exponential-Exponential Distribution

Abdullahi, Jamila; Abdullahi, Umma; Ieren, Terna Godfrey; Kuhe, David Adugh; Umar, Adamu Abubakar

doi:10.9734/ajpas/2018/v1i424552

Cited by 14 publications

(15 citation statements)

References 23 publications

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“…The result is an affirmation to the fact that the method quadratic rank transmutation map for adding a single or transmuted parameter has increased the flexibility of the power Gompertz distribution. Hence, this study is in line with the conclusions in [33,36,37,38,39,40,49,50,51].…”

Section: Data Set Isupporting

confidence: 92%

“…It has been discovered that using the quadratic rank transmutation map by [8] brings about more skewed and flexible probability distributions with an additional shape parameter usually known as the transmuted parameter and this process has led to many flexible compound distributions such as the Transmuted Gompertz distribution [33], the transmuted exponential Lomax distribution by [36], the transmuted odd generalized exponential exponential distribution [37], the transmuted Weibull-Exponetial distribution [38], the transmuted Lindley-Exponential distribution [39] and the transmuted Weibull-Rayleigh distribution [40].…”

Section: Introductionmentioning

confidence: 99%

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Properties and Applications of a Transmuted Power Gompertz Distribution

Eraikhuemen

Chama

Asongo

et al. 2020

AJPAS

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This article introduces and studies a new probability distribution called “Transmuted Power Gompertz distribution”. It looks at the properties of the transmuted power Gompertz distribution. The article also estimates the four parameters of the new model using the method of maximum likelihood estimation. The article further evaluates the goodness-of-fit of the proposed distribution compared to other distributions by means of applications of the model to two real life datasets and the result show that the proposed distribution is more flexible than the fitted existing distributions.

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Section: Data Set Isupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Properties and Applications of a Transmuted Power Gompertz Distribution

Eraikhuemen

Chama

Asongo

et al. 2020

AJPAS

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“…Dataset: This data represents the remission times (in months) of a random sample of 128 bladder cancer patients adopted from the work of Rady et al [36]. It has previously been used by Lee and Wang [40], Rady et al [36], Ieren and Chukwu [41] and Abdullahi et al [42]. It is given and summarized as follows: 0.080, 0.200, 0.400, 0.500, 0.510, 0.810, 0.900, 1.050, 1.190…”

Section: Applications To Three Real Life Datasetsmentioning

confidence: 99%

A Power Gompertz Distribution: Model, Properties and Application to Bladder Cancer Data

Ieren

Kromtit

Agbor

et al. 2019

ARJOM

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This paper uses a power transformation approach to introduce a three-parameter probability distribution which gives another extension of the Gompertz distribution known as “Power Gompertz distribution”. The statistical features of the power Gompertz distribution are systematically derived and studied appropriately. The three parameters of the new model are being estimated using the method of maximum likelihood estimation. The proposed distribution has also been compared to the Gompertz distribution using a real life dataset and the result shows that the Power Gompertz distribution has better performance than the Gompertz distribution and hence will be more useful and effective if applied in some real life situations especially survival analysis and cure fraction modeling just like the conventional Gompertz distribution.

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“…This data set represents the remission times (in months) of a random sample of 128 bladder cancer patients. It has previously been used in [21,22,23]. It is summarized as follows: From the descriptive statistics in Table 1, it is observed that the data set is positively skewed with a very high coefficient of kurtosis and therefore suitable for flexible and skewed distributions.…”

Section: Data Setmentioning

confidence: 99%

A Weibull-Gompertz Makeham Distribution with Properties and Application to Cancer Data

Koleoso

Chukwu

2019

JAMCS

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The article presents an extension of the Gompertz Makeham distribution using the Weibull-G family of continuous probability distributions proposed by Tahir et al. (2016a). This new extension generates a more flexible model called Weibull-Gompertz Makeham distribution. Some statistical properties of the distribution which include the moments, survival function, hazard function and distribution of order statistics were derived and discussed. The parameters were estimated by the method of maximum likelihood and the distribution was applied to a bladder cancer data. Weibull-Gompertz Makeham distribution performed best (AIC = -6.8677, CAIC = -6.3759, BIC = 7.3924) when compared with other existing distributions of the same family to model bladder cancer data.

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On the Properties and Applications of Transmuted Odd Generalized Exponential-Exponential Distribution

Cited by 14 publications

References 23 publications

Properties and Applications of a Transmuted Power Gompertz Distribution

Properties and Applications of a Transmuted Power Gompertz Distribution

A Power Gompertz Distribution: Model, Properties and Application to Bladder Cancer Data

A Weibull-Gompertz Makeham Distribution with Properties and Application to Cancer Data

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