Extended Gumbel Type-2 Distribution: Properties and Applications

Ogunde, Adebisi Ade; Fayose, S. T.; Ajayi, B.; Omosigho, D. O.

doi:10.1155/2020/2798327

Cited by 11 publications

(8 citation statements)

References 22 publications

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“…Exponentiated Gumbel type-II distributed was discovered by Biswas and Gupta [22]. Gillariose et al [23] introduced a new Marshall-Olkin Modified Lindley Distribution; Marshall Olkin Power Lomax distribution was proposed by Haq et al [24]; Ogunde et al [25] proposed the Extended Gumbel Type-II distribution; and Almetwally et al [26] derived various properties of Marshall-Olkin Alpha Power Weibull distribution.…”

Section: Introductionmentioning

confidence: 99%

Marshall–Olkin Extended Gumbel Type‐II Distribution: Properties and Applications

Willayat¹,

Saud

Ijaz

et al. 2022

Complexity

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Due to the advance computer technology, the use of probability distributions has been raised up to solve the real life problems. These applications are found in reliability engineering, computer sciences, economics, psychology, survival analysis, and some others. This study offers a new probability model called Marshall–Olkin Extended Gumbel Type-II (MOEGT-II) which can model various shapes of the failure rate function. The proposed distribution is capable to model increasing, decreasing, reverse J-shaped, and upside down bathtub shapes of the failure rate function. Various statistical properties of the proposed distribution are derived such as alternate expressions for the density and distribution function, special cases of MOEGT-II distribution, quantile function, Lorenz curve, and Bonferroni curve. Estimation of the unknown parameters is carried out by the method of maximum likelihood. A simulation study is conducted using three different iterative methods with different samples of sizes n. The usefulness and potentiality of the MOEGT-II distribution have been shown using three real life data sets. The MOEGT-II distribution has been demonstrated as better fit than Exponentiated Gumbel Type-II (EGT-II), Marshall–Olkin Gumbel Type-II (MOGT-II), Gumbel Type-II (GT-II), Marshall–Olkin–Frechet (MOF), Frechet (F), Burr III, Log Logistic (LL), Beta Inverse Weibull (BIW), and Kumaraswamy Inverse Weibull (KIW) distributions.

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Section: Introductionmentioning

confidence: 99%

Marshall–Olkin Extended Gumbel Type‐II Distribution: Properties and Applications

Willayat¹,

Saud

Ijaz

et al. 2022

Complexity

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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%

“…This dataset is the remission times (in months) of a random sample of 128 patients with bladder cancer. It has been widely applied by notable researchers and recently by leren et al (2019) [9] , Ogunde et al (2020) [13] using EGTT and Ogunde et al (2021) [14] in the study of GGTT distributions. The LGTT distribution is applied to the data and compared with some notable generalizations of the Gumbel Type-two distribution GGTT, EGTT, ExGTT, ETGTT, and GTT distributions.…”

Section: Data 5: Bladder Cancer 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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“…6 shows a concave transform, indicating that the hrf is possibly increasing. Thus for the glass fibre data we compare its fits with the following distribution: Exponentiated Gumbel Type-2 (ExGT-2) distribution by Okorie et al [1], Extended Gumbel Type-2 (EGT-2) distribution by Ogunde et al [3]. The MLEs of the parameters of these distributions considered and their corresponding standard errors (SEs) for survival times of pig's data are provided in Table 5 the statistics AIC, BIC, CAIC and HQIC are listed in Table 6.…”

Section: Glass Fibre Datamentioning

confidence: 99%

“…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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Extended Gumbel Type-2 Distribution: Properties and Applications

Cited by 11 publications

References 22 publications

Marshall–Olkin Extended Gumbel Type‐II Distribution: Properties and Applications

Marshall–Olkin Extended Gumbel Type‐II Distribution: Properties and Applications

Lomax gamble type two distributions with applications to lifetime data

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

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