Beta Kumaraswamy Burr Type X Distribution and Its Properties

Madaki, Umar Yusuf; Bakar, Mohd Rizam Abu; Handique, Laba

doi:10.20944/preprints201808.0356.v1

Cited by 4 publications

(4 citation statements)

References 24 publications

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“…Generalized inverted K-G Physics data [72] Topp-Leone generalized inverted K model Physics data [73] K log-logistic Weibull model Failure times data [74] Exponentiated inverse K model Economic data [75] Beta K Burr Type X model Physics and medical data [76] Marshall-Olkin extended inverted K model Physics, failure and medical data [77] K generalized Kappa model Geological data [78] Cubic rank transmuted K model Food and industrial data [79] K Marshall-Olkin log-logistic model Physics data [67] Odd generalized exponential K model Geological and environmental data [80] K exponentiated U-quadratic model Medical data [81] K odd Burr-G Physics and engineering data [82] Exponentiated generalized K model Environmental, agriculture and engineering data [83] Size-biased K model Engineering data [84] K generalized power Weibull model Engineering data [85] Exponentiated K-Dagum model Income and lifetime data [61]…”

Section: Model Modeling Authorsmentioning

confidence: 99%

A New Extension of the Kumaraswamy Exponential Model with Modeling of Food Chain Data

Eldessouky

Hassan

Elgarhy

et al. 2023

Axioms

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Statistical models are useful in explaining and forecasting real-world occurrences. Various extended distributions have been widely employed for modeling data in a variety of fields throughout the last few decades. In this article we introduce a new extension of the Kumaraswamy exponential (KE) model called the Kavya–Manoharan KE (KMKE) distribution. Some statistical and computational features of the KMKE distribution including the quantile (QUA) function, moments (MOms), incomplete MOms (INMOms), conditional MOms (COMOms) and MOm generating functions are computed. Classical maximum likelihood and Bayesian estimation approaches are employed to estimate the parameters of the KMKE model. The simulation experiment examines the accuracy of the model parameters by employing Bayesian and maximum likelihood estimation methods. We utilize two real datasets related to food chain data in this work to demonstrate the importance and flexibility of the proposed model. The new KMKE proposed distribution is very flexible, more so than numerous well-known distributions.

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Section: Model Modeling Authorsmentioning

confidence: 99%

A New Extension of the Kumaraswamy Exponential Model with Modeling of Food Chain Data

Eldessouky

Hassan

Elgarhy

et al. 2023

Axioms

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“…Although, this model received well attention over the years but one of its shortcomings comes when the covariates were modelled using the proportion p which does not involve a proportional hazard (PH) model, as it is the property of survival model which corresponds to the models' involving covariates. On the other hand, Martinez, Achcar, Jacome and Santos (2013), makes some observations in equation (1), as it provides an improper posterior result which includes the non-informative prior (uniform). It is actually the main reason for its drawback in the recent years which makes its tedious to apply the Bayesian technique with non-informative priors which is very common nowadays.…”

Section: Cure Rate Modelsmentioning

confidence: 99%

“…We observed that the function cannot be expressed in a closed form reference to the limitation propose by . The baseline probability density function of the Beta Kumaraswamy Burr X distribution with six parameters by Madaki et al (2018), is written as follows:…”

Section: Beta Kumaraswamy Burr X Model Formulationmentioning

confidence: 99%

A Bayesian Parametric Estimation of Beta Kumaraswamy Burr Type X (Beta Kum-BX) Distribution Based on Cure Models with Covariates

Madaki

Bakar

2022

JST

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In statistical models for censored survival data which includes a proportion of individuals who are not subject to the event of interest under study are known as the long-term survival cured models. It has two most adopted and common models used in estimating the cure fraction namely: the mixture (standard cure) and the non-mixture models. In this research work, we introduce a Bayesian approach using the two models for survival data based on the Beta Kumaraswamy Burr Type X distribution with six parameters and compared with two existing models: beta-Weibull and beta-generalized exponential distributions in analyzing a real-life dataset. The proposed approach allows the inclusion of covariates in the model. The parameter estimation was obtained by maximum likelihood and Bayesian analysis methods. The win Bugs and MCMC pack library in R softwares were employed for the Gibbs sampling algorithm in other to obtain the posterior summaries of interest and also the trace plots by the applying of real data sets and a simulation study was done based on cure models to compare the performance of both models relating to actual sense of motivation and novelty which clarifies the usefulness of the proposed methodologies.

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“…Moreover, two methods of estimation were discussed. Reference [15] continued some studies on the beta generated Kumaraswamy-G family by proposing the beta generated Kumaraswamy Burr type X distribution. Some properties of the distribution were provided; the maximum likelihood estimation was also obtained, and the model was applied to a real data set.…”

Section: Introductionmentioning

confidence: 99%

Characterizations of the Beta Kumaraswamy Exponential Distribution

2019

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In this article, the five-parameter beta Kumaraswamy exponential distribution (BKw-E) is introduced, and some characterizations of this distribution are obtained. The shape of the hazard function and some other important properties—such as median, mode, quantile function, and mean—are studied. In addition, the moments, skewness, and kurtosis are found. Furthermore, important measures such as Rényi entropy and order statistics are obtained; these have applications in many fields. An example of a real data set is discussed.

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Beta Kumaraswamy Burr Type X Distribution and Its Properties

Cited by 4 publications

References 24 publications

A New Extension of the Kumaraswamy Exponential Model with Modeling of Food Chain Data

A New Extension of the Kumaraswamy Exponential Model with Modeling of Food Chain Data

A Bayesian Parametric Estimation of Beta Kumaraswamy Burr Type X (Beta Kum-BX) Distribution Based on Cure Models with Covariates

Characterizations of the Beta Kumaraswamy Exponential Distribution

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