Truncated Weibull-G More Flexible and More Reliable than Beta-G Distribution

Najarzadegan, Hossein; Alamatsaz, Mohammad Hossein; Hayati, Saied

doi:10.5539/ijsp.v6n5p1

Cited by 19 publications

(13 citation statements)

References 14 publications

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“…Therefore, we have a truncated general-G class of distributions and the only sub-model we aware of is the truncated Weibull -G family proposed by Najarzadegan et al [1]. As a powerful alternative to beta-G family of distributions.…”

Section: Introductionmentioning

confidence: 99%

Statistical Inference of Truncated Weibull-Rayleigh Distribution: Its Properties and Applications

Khalifa¹,

Ramadan²,

El-Desouky³

2021

OJMSi

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In this paper, we derived a new distribution named as truncated Weibull Rayleigh (TW-R) distribution. Its characterization and statistical properties are obtained, such as reliability function, hazard function, reversed hazard rate function, cumulative hazard rate function, quantile function, rth moment, incomplete moments, Rényi and q entropies and order statistic. Parameter estimation is implemented using method of maximum-likelihood estimation and Fisher information matrix is derived. Finally, application of the presented new distribution to a real data representing the failure times of 63 airbcraft Windshield is given and its goodness-of-fit is demonstrated. In addition to, comparisons to other models are implemented to show the flexibility of the presented model.

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Section: Introductionmentioning

confidence: 99%

Statistical Inference of Truncated Weibull-Rayleigh Distribution: Its Properties and Applications

Khalifa¹,

Ramadan²,

El-Desouky³

2021

OJMSi

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“…In this paper, we contribute to the capability of the M distribution to generate flexible distributions by considering the truncated-composed scheme. A list of recent families of distributions based on this scheme is the following: truncated Fréchet generated (TFG) family by Abid and Abdulrazak [11], truncated Weibull generated (TWG) family by Najarzadegan et al [12], truncated inverted Kumaraswamy generated (TIKG) family by Bantan et al [13], type II truncated Fréchet generated (TIITFG) family by Aldahlan [14], truncated Cauchy power generated (TCPG) family by Aldahlan et al [15], exponentiated truncated inverse Weibull generated (ETIWG) family by Almarashi et al [16], truncated Burr generated (TBG) family by Jamal et al [17], truncated generalized Fréchet generated (TGFG) family by ZeinEldin et al [18], truncated inverse Lomax generated (TILG) family by Algarni et al [19], and truncated Burr X generated (TBX) family by Bantan et al [20]. Despite this extensive literature, there is no work on what can be called the new truncated M generated (NTM-G) distribution.…”

Section: Introductionmentioning

confidence: 99%

A New Truncated Muth Generated Family of Distributions with Applications

et al. 2021

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In recent years, the Muth distribution has been used for the construction of accurate statistical models, with applications in various applied fields. In this paper, we use a truncated-composed scheme to create a new unit Muth distribution, from which we motivate a more general family of continuous distributions called the truncated Muth generated family. The key benefits of this family are its analytical simplicity, connections with the exponential generated family, and flexibility conferred on any parental distribution. In particular, it improves the capability of the functions of the parental distribution, enhancing their peak, asymmetry, tail, and flatness levels, among others. The characteristics of quantile and moment measures and functions of the truncated Muth generated family are described in detail. As a concrete example, a particular distribution that extends the Weibull distribution is highlighted. In an applied part, the parameters are calculated using the maximum likelihood procedure. We use a comprehensive simulation analysis to demonstrate the accuracy of the derived estimates. The revised Weibull model is then used to fit two real-world datasets. The new model is shown to be more suited to these datasets than other competing models.

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“…Some of the authors used the truncated models as the generators. Abid and Abdulrazak [1] used truncated Fréchet distribution on (0, 1) to get the truncated Fréchet-G family, Najarzadegan et al [7] who proposed the truncated Weibull-G family by using the (0, 1) truncated Weibull model, Bantan et al [4] who studied the truncated inverted Kumaraswamy -G family by using the (0,1) truncated inverted Kumaraswamy distribution. Recently, Aldahlan [2] has used (0, 1) truncated Fréchet distribution to get the type II truncated Fréchet generated family of distributions and it has the following distribution function (cdf) and probability density function (pdf) nential (IE) model.…”

Section: Introductionmentioning

confidence: 99%

Estimation of type II truncated Fr'echet inverse exponential distribution under censored data

Aldahlan¹

2020

J. Nonlinear Sci. Appl.

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This paper proposes a new probability distribution called as type II truncated Fréchet inverse exponential distribution. Fundamental statistical properties like moments, incomplete moments, and quantile function of the proposed model are studied. The estimation of the parameter of the new model is approached by maximum likelihood method based on complete and censored samples. Asymptotic confidence interval of model parameters is investigated. We assess the numerical results to study the theoretical results. Superiority of the type II truncated Fréchet inverse exponential model over some known distributions is showed through one real data set.

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Truncated Weibull-G More Flexible and More Reliable than Beta-G Distribution

Cited by 19 publications

References 14 publications

Statistical Inference of Truncated Weibull-Rayleigh Distribution: Its Properties and Applications

Statistical Inference of Truncated Weibull-Rayleigh Distribution: Its Properties and Applications

A New Truncated Muth Generated Family of Distributions with Applications

Estimation of type II truncated Fr'echet inverse exponential distribution under censored data

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