The Gompertz Gumbel II Distribution: Properties and Applications

Ogunde, Adebisi Ade; Olalude, Gbenga Adelekan; Omosigho, D. O.

doi:10.32861/ajams.71.1.15

Cited by 3 publications

(6 citation statements)

References 5 publications

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“…The fourth data represents survival times (in days) of 72 guinea pigs infected with virulent tubercle bacilli, reported by Bjerkedal (1960) [2] . It has received several applications and recently by Ogunde et al (2020) [13] for testing the flexibility of Extended Gumbel Type-two (EGTT) and Ogunde et al (2021) [14] in Gompertz Gumbel Type-two (GGTT) distributions. Results of analysis on Table 6 and graphical plots of density function with the estimated cdfs displayed in Figure 6 shows that LGTT distribution can be considered as better model for the data set than all the competitive distributions derived by extensions of the GTT distribution.…”

Section: Data 4: Guinea Pigs Datamentioning

confidence: 99%

Lomax gamble type two distributions with applications to lifetime data

Adeyemi¹,

Adeleke²,

Akarawak³

2022

Int. J. Stat. Appl. Math.

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The Gumbel Type-Two (GTT) distribution among many classical distributions has been found to lack the capacity to adequately fit some random phenomenon due to its monotonic failure rates thereby limiting its application. The Lomax-Generator was employed in this study to generalize the GTT distribution in order to derive the Lomax Gumbel Type-Two (LGTT) distribution capable of providing better modeling fits to real dataset. The new distribution has the Lomax Inverse Exponential distribution as a special case; the reliability and hazard rates functions were investigated. The distribution is unimodal, positively skewed and close to bell shape depending on parameter values. The quantile, median and order statistics were derived while the method of maximum likelihood estimation was used for estimating the parameters of the distribution. The proposed distribution demonstrated its potentials for modeling events whose distributions tends to be platykurtic, leptokurtic and approximately symmetric. Two lifetime survival datasets were analyzed using the distribution and results revealed the importance of application of LGTT distribution to the datasets with superior modeling fits than other four generalizations of GTT distributions existing in literatures derived using other generator. Three additional real life datasets were also used to compare the performance of LGTT with some distributions derived using Lomax as generator, results of analysis revealed that LGTT exhibits greater flexible potentials for modelling the real life datasets.

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Section: Data 4: Guinea Pigs Datamentioning

confidence: 99%

Lomax gamble type two distributions with applications to lifetime data

Adeyemi¹,

Adeleke²,

Akarawak³

2022

Int. J. Stat. Appl. Math.

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“…This dataset from Lee and Wang [32] is the remission (in months) of a random sample of 128 patients with bladder cancer. It has been widely applied by notable researchers to test the performance of many newly developed convoluted probability distributions including Salem [13], Elbatal et al [33], Ateeq et al [34], leren et al [17] using PG, and most recently by Ogunde et al [28] using GGTT. The GEP distribution is applied to the data and compared with EP, EEP, KEP, GGTT, GoIE, GoLom and the (PG) Power Gompertz distributions.…”

Section: Application To Bladder Cancer Datamentioning

confidence: 99%

“…Koleoso et al [22] developed a three parameter Gompertz Lindley distribution, and Gompertz Flexible Weibull (GoFW) was developed by Khaleel et al [23], the Gompertz extended generalized exponential distribution by Eghwerido et al [24], Gompertz Lomax (GoLom) by Oguntunde et al [25], Gompertz Alpha-Power Inverted Exponential (GAPIE) distribution by Eghwerido, et al [26]. [27] developed the Gompertz Rayleigh distribution and most recently, Ogunde et al [28] developed Gompertz Gumbel type II (GGTT). The usefulness of these proposed distributions was investigated by way of applications to real-life datasets.…”

Section: Introductionmentioning

confidence: 99%

“…The usefulness of these proposed distributions was investigated by way of applications to real-life datasets. [5,6] and [14] applied the distributions to the Wheaton River data while the EEP by [13], GGTT by [28] and, the (PG) distributions by [17] justified the importance of the proposed distributions by application to the bladder cancer dataset. However, the existing results can still be improved because the dataset has not received adequate model fit from the distributions.…”

Section: Introductionmentioning

confidence: 99%

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The gompertz exponential pareto distribution with the properties and applications to bladder cancer and hydrological datasets

Adeyemi

Akarawak

Adeleke

2021

CST

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Many existing distributions in literatures does not have the modeling fits capacity to adequately describe the real-life phenomena. The Exponential Pareto (EP) distribution has further gained some generalizations among several authors using different generator techniques with an aim to obtain a new distribution with greater flexibility. This article proposes Gompertz Exponential Pareto (GEP) distribution using the Gompertz generator. Findings from the study revealed some lifetime distributions as special cases and mathematical properties of the distribution investigated including the mean, variance, coefficient of variation, quantile, moment, moment generating function and, order statistics. The distribution can be positively or negatively skewed. It is unimodal with failure rates whose shapes could be reversed J bathtub, constant, decreasing and, increasing and the parameters were estimated using maximum likelihood estimation approach. Applications to two real-life datasets revealed the ability of GEP distribution to provide more flexibilities and better fit to the dataset compared to some previously proposed distributions for the data. The results also revealed that GEP had the superior performance over other generalizations of EP distribution existing in literatures and the performance has further strengthened the usefulness of the Gompertz-generator technique.

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“…The Kumaraswamy Gumbel type-2 was developed and studied by Okorie et al [2]. Recently, Ogunde et al [3] proposed and studied the properties of Extended Gumbel type-2 distribution and Gompertz Gumbel type-2 distribution was studied by Ogunde et al [4]. The cumulative distribution function is given by ( ; , ) = ; , > 0 (1)…”

Section: Introductionmentioning

confidence: 99%

Type II Topp-Leone Gumbel Type-2 distribution: Properties and Applications

Olayode¹,

Ademola²,

Friday³

2021

ARJOM

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In this paper, a three parameter life time model named Type II Topp-Leone Gumbel type-2 distribution which can be used to model reliability problems, fatigue life studies, and survival data has been studied. We derived explicit expressions for some of its statistical properties such as ordinary moments, generating function, incomplete moments, and order statistics. The maximum likelihood estimation technique is used to estimate the parameters of the model. The tractability of the model was illustrated by using two real life data sets. The proposed distribution provides a better fit than some well known distributions using criteria of criteria of goodness of fit.

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The Gompertz Gumbel II Distribution: Properties and Applications

Cited by 3 publications

References 5 publications

Lomax gamble type two distributions with applications to lifetime data

Lomax gamble type two distributions with applications to lifetime data

The gompertz exponential pareto distribution with the properties and applications to bladder cancer and hydrological datasets

Type II Topp-Leone Gumbel Type-2 distribution: Properties and Applications

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