Theoretical Analysis of the Weibull Alpha Power Inverted Exponential Distribution: Properties and Applications

Efe-Eyefia, Eferhonore; Thomas, J.; Zelibe, Samuel Chiabom

doi:10.35378/gujs.537832

Cited by 24 publications

(9 citation statements)

References 32 publications

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“…The observations are as follows: 0.55, 0.74, 0.77, 0.81, 0.84, 1.24, 0.93, 1.04, 1.11, 1.13, 1.30, 1.25, 1.27, 1.28,1.29, 1.48, 1.36, 1.39, 1.42, 1.48, 1.51, 1.49, 1.49, 1.50, 1.50,1.55, 1.52, 1.53, 1.54, 1.55, 1.61, 1.58, 1.59, 1.60, 1.61, 1.63,1.61, 1.61, 1.62, 1.62, 1.67, 1.64, 1.66, 1.66, 1.66, 1.70, 1.68,1.68, 1.69, 1.70, 1.78, 1.73, 1.76, 1.76, 1.77, 1.89, 1.81, 1.82,1.84, 1.84, 2.00, 2.01, 2.24. Recently, the dataset had been studied by Abouelmagd et al [24], Mead et al [25], Zelibe et al [26] and Eghwerido [9].…”

Section: Applicationmentioning

confidence: 99%

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Statistical Modeling using A New Hybrid Form of The Inverted Exponential Distribution with Different Estimation Methods

Adubisi¹,

Adubisi

2022

InPrime:Ind.Jour.Pure.Applied.Math

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This paper introduces a new four-parameter distribution called the exponentiated Gompertz generated inverted exponential (EGGIE) distribution. Explicit expressions of the structural properties such as the ordinary and incomplete moments, probability weighted moments, quantile function, Lorenz and Bonferroni curves, entropies, and order statistics are derived. The empirical findings indicate that the maximum likelihood procedure dominates the other estimators in the simulation study while the Cramer-Von Mises procedure dominates in the two real datasets applications. We demonstrate the superiority of the EGGIE distribution over the Gompertz Lomax, odd Fréchet Inverse exponential, generalized inverse exponential, generalized inverse exponential, exponential inverse exponential, and Gompertz Weibull distribution using the maximum likelihood procedure utilizing two real datasets applications. The findings show that the EGGIE distribution yields the best goodness of fit to the two datasets.Keywords: exponentiated Gompertz generated family; inverse exponential distribution; Kolmogorov-Smirnov statistic; Anderson-Darling; maximum product spacing. AbstrakPaper ini memperkenalkan distribusi 4-parameter baru yang disebut dengan distribusi exponentiated Gompertz generated inverted exponential (EGGIE). Ekspresi eksplisit sifat struktural dari distribusi ini diturunkan, seperti momen biasa dan momen tak lengkap, momen probabilitas terboboti, fungsi kuartil, kurva Lorenz dan Bonferroni, entropi, dan statistik urutan. Temuan empiris menunjukan bahwa prosedur maksimum likelihood mendominasi estimator lainnya pada studi simulasi, sementara prosedur Cramer-Von Mises mendominasi pada aplikasi dua dataset nyata. Peneliti menunjukkan keunggulan dari distribusi EGGIE dibandingkan distribusi Gompertz Lomax, odd Frechet Inverse exponential, generalized inverse exponential, exponential inverse exponential, dan Gompertz Weibull menggunakan metode maksimum likelihood yang diaplikasikan pada dua dataset nyata. Hasil menunjukan bahwa distribusi EGGIE menghasilkan kecocokan model yang baik pada kedua dataset.Kata Kunci: keluarga bangkitan exponentiated Gompertz; distribusi inverse exponential; Kolmogorov-Smirnov statistic; Anderson-Darling; maximum product spacing. 2020MSC: 62E10

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Section: Applicationmentioning

confidence: 99%

“…The flexibility of the distribution using the MLE was demonstrated utilizing three real datasets. Eghwerido [9] introduced the Weibull IE distribution with expressions of the moments, probability weighted moments, quantile function and then demonstrated its flexibility over some distributions using the MLE procedure.…”

Section: Introductionmentioning

confidence: 99%

Statistical Modeling using A New Hybrid Form of The Inverted Exponential Distribution with Different Estimation Methods

Adubisi¹,

Adubisi

2022

InPrime:Ind.Jour.Pure.Applied.Math

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“…The power Lomax distribution is more flexible with respect to existing Lomax distribution with decreasing and inverted bathtub hazard rate functions [Rady et al (2016)]. Taking alpha as exponent in inverted exponential distribution [Ceren et al (2018)], a new distribution named alpha power inverted exponential distribution was proposed. Odd Lomax exponential (type III) model was introduced using the Lomax random variable as a generator [Ogunsanya et al(2019)] .…”

Section: Introductionmentioning

confidence: 99%

Modified Lomax Distribution: Properties and Applications

Telee,

Yadav,

Kumar

2024

Preprint

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Here, a new distribution having three called Modified Lomax Distribution is proposed. Some important statistical properties of the introduced distribution are explained ere. The Least Square method of Estimation (LSE), Cramer-Von Mises method of estimation (CVM) and Maximum Likelihood method (MLE) methods are used for estimation of the parameter using R programming software. A data set is discussed and performed the goodness-of-fit to assess the application of the proposed distribution. Various methods of model comparison and model validation are also used. The proposed model called Modified Lomax Distribution is better than some earlier defined probability model.

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“…For instance, Bhati et al 13 were the ones who rst recommended using the Lindley exponential distribution. and extended new generalized exponential distribution was proposed by Eghwerido et al 14. For further reading on the extensions of the exponential distribution, the reader is referred to [15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Exponentiated Gull Alpha Exponential Distribution with Application to COVID‐19 Data

Khogeer

Alrumayh

El‐Raouf

et al. 2022

Journal of Mathematics

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In this paper, the main aim is to define a statistical distribution that can be used to model COVID-19 data in Mexico and Canada. Using the method of exponentiation on the gull alpha exponential distribution introduces a new distribution with three parameters called the exponentiated gull alpha power exponential (EGAPE) distribution. The distribution has the benefit of being able to represent monotonic and nonmonotonic failure rates, both of which are often seen in dependability issues. It is possible to determine the quantile function as well as the skewness, kurtosis, and order statistics of the suggested distribution. The approach of maximum likelihood is used in order to calculate the parameters of the model, and the RMSE and average bias are utilised in order to evaluate how successful the strategy is. In conclusion, the flexibility of the new distribution is demonstrated by modeling COVID-19 data. From the practical application, we can conclude that the proposed model outperformed the competing models and therefore can be used as a better option for modeling COVID-19 and other related datasets.

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Theoretical Analysis of the Weibull Alpha Power Inverted Exponential Distribution: Properties and Applications

Cited by 24 publications

References 32 publications

Statistical Modeling using A New Hybrid Form of The Inverted Exponential Distribution with Different Estimation Methods

Statistical Modeling using A New Hybrid Form of The Inverted Exponential Distribution with Different Estimation Methods

Modified Lomax Distribution: Properties and Applications

Exponentiated Gull Alpha Exponential Distribution with Application to COVID‐19 Data

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