Extended Weibull log-logistic distribution

Abouelmagd, T. H. M.; Hamed, M. S.; Almamy, Jehan. A. Almamy; Ali, M. Masoom; Yousof, Haitham M.; Korkmaz, Mustafa Ç.

doi:10.22436/jnsa.012.08.03

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…This real data set gives the survival times, in weeks, of 33 patients suffering from acute myelogenous leukemia. Those real data sets were recently analysed by Altun et al (2018a), Altun et al (2018b), Abouelmagd et al (2019), Gad et al (2019 and Yousof (2019, 2021). The total time test (TTT) plot (Aarset, 1987) is an important graphical approach to verify whether the data can be applied to a specific distribution or not.…”

Section: Comparing Models Under Complete Samplesmentioning

confidence: 99%

The exponential generalized log-logistic model: Bagdonavičius-Nikulin test for validation and non-Bayesian estimation methods

Ibrahim

Aidi

Ali

et al. 2022

Communications for Statistical Applications and Methods

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A modified Bagdonavičius-Nikulin chi-square goodness-of-fit is defined and studied. The lymphoma data is analyzed using the modified goodness-of-fit test statistic. Different non-Bayesian estimation methods under complete samples schemes are considered, discussed and compared such as the maximum likelihood least square estimation method, the Cramer-von Mises estimation method, the weighted least square estimation method, the left tail-Anderson Darling estimation method and the right tail Anderson Darling estimation method. Numerical simulation studies are performed for comparing these estimation methods. The potentiality of the new model is illustrated using three real data sets and compared with many other well-known generalizations.

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Section: Comparing Models Under Complete Samplesmentioning

confidence: 99%

The exponential generalized log-logistic model: Bagdonavičius-Nikulin test for validation and non-Bayesian estimation methods

Ibrahim

Aidi

Ali

et al. 2022

Communications for Statistical Applications and Methods

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“…The two data sets are displayed in Tables 12 and 13. The Ex-LL distribution is compared with some competing distributions including the alpha power log-logistic (APLL) [30], transmuted log-logistic (TLL) [31], generalized log-logistic (GLL) [32], Marshall-Olkin log-logistic (MOLL) [13], Poisson Burr-X log-logistic (PBXLL) [33], transmuted inverse log-logistic (TILL) [34], inverse log-logistic (ILL) [34], Weibull generalized log-logistic (WGLL) [35], and LL distributions.…”

Section: Modeling Real Data From the Engineering And Insurance Fieldsmentioning

confidence: 99%

The Extended Log-Logistic Distribution: Inference and Actuarial Applications

et al. 2021

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Actuaries are interested in modeling actuarial data using loss models that can be adopted to describe risk exposure. This paper introduces a new flexible extension of the log-logistic distribution, called the extended log-logistic (Ex-LL) distribution, to model heavy-tailed insurance losses data. The Ex-LL hazard function exhibits an upside-down bathtub shape, an increasing shape, a J shape, a decreasing shape, and a reversed-J shape. We derived five important risk measures based on the Ex-LL distribution. The Ex-LL parameters were estimated using different estimation methods, and their performances were assessed using simulation results. Finally, the performance of the Ex-LL distribution was explored using two types of real data from the engineering and insurance sciences. The analyzed data illustrated that the Ex-LL distribution provided an adequate fit compared to other competing distributions such as the log-logistic, alpha-power log-logistic, transmuted log-logistic, generalized log-logistic, Marshall–Olkin log-logistic, inverse log-logistic, and Weibull generalized log-logistic distributions.

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“…The statistical properties of the EE model have been studied by many authors. Many authors have derived and studied the EE model, see Zheng [66], Zheng and Park [67], Kundu and Pradhan [48], Aslam et al [15], Aryal et al [13], Khalil et al [42], Abouelmagd et al ([1], [2]), Ibrahim et al [35] and Bhatti et al [16] among others. Recently, Alizadeh et al [6] defined a new family based on the exponential model called the generalized odd generalized exponential family of distributions.…”

Section: Introductionmentioning

confidence: 99%

A New Version of the Exponentiated Exponential Distribution: Copula, Properties and Application to Relief and Survival Times

Elgohari

2021

Stat., optim. inf. comput.

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In this paper, we introduce a new generalization of the Exponentiated Exponential distribution. Various structural mathematical properties are derived. Numerical analysis for mean, variance, skewness and kurtosis and the dispersion index is performed. The new density can be right skewed and symmetric with "unimodal" and "bimodal" shapes. The new hazard function can be "constant", "decreasing", "increasing", "increasing-constant", "upside down-constant", "decreasing nstant". Many bivariate and multivariate type model have been also derived. We assess the performance of the maximum likelihood method graphically via the biases and mean squared errors. The usefulness and flexibility of the new distribution is illustrated by means of two real data sets.

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Extended Weibull log-logistic distribution

Cited by 6 publications

References 5 publications

The exponential generalized log-logistic model: Bagdonavičius-Nikulin test for validation and non-Bayesian estimation methods

The exponential generalized log-logistic model: Bagdonavičius-Nikulin test for validation and non-Bayesian estimation methods

The Extended Log-Logistic Distribution: Inference and Actuarial Applications

A New Version of the Exponentiated Exponential Distribution: Copula, Properties and Application to Relief and Survival Times

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