The Generalized Weibull-Burr Xii Distribution and Its Applications

Maksaei, Najmieh; Altun, Emrah

doi:10.6339/jds.201707_15(3).0009

Journal of Data Science

2021

DOI: 10.6339/jds.201707_15(3).0009

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The Generalized Weibull-Burr Xii Distribution and Its Applications

Najmieh Maksaei¹,

Emrah Altun²

Abstract: In this paper, we introduce a new lifetime model, called the Generalized Weibull-Burr XII distribution. We discuss some of its mathematical properties such as density, hazard rate functions, quantile function and moments. Maximum likelihood method is used to estimate model parameters. A simulation study is performed to assess the performance of maximum likelihood estimators by means of biases, mean squared errors. Finally, we prove that the proposed distribution is a very competitive model to other classical m… Show more

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Cited by 2 publications

References 27 publications

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Marshall–Olkin Weibull–Burr XII distribution with application to physics data

Alsadat,

Nagarjuna,

Hassan

et al. 2023

AIP Advances

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The focus of this study is a new lifetime distribution with five parameters created by combining the Weibull–Burr XII model and the Marshall–Olkin-G family. The newly suggested model is known as the Marshall–Olkin Weibull–Burr XII (MOWBXII) distribution. The new distribution has the benefit of being able to model different types of data, and it is useful in reliability and lifespan statistics. Several current distributions as well as new distributions are included in the MOWBXII distribution. The MOWBXII density function is represented as a linear combination of Burr XII densities. Some statistical properties of the MOWBXII distribution are discussed. Various techniques for estimating the model’s parameters are used. The proposed estimation methods are weighted least squares, maximum likelihood, least squares, and maximum product of spacing methods. The effectiveness of different estimates is evaluated in terms of relative bias and mean squared error of the simulation study. Practical illustrations of the MOWBXII distribution are demonstrated using two real datasets. Furthermore, it is shown that the proposed distribution fits well, and this is claimed by comparing with Burr XII-based distributions and some other distributions by means of some measures of goodness-of-fit.

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Marshall–Olkin Weibull–Burr XII distribution with application to physics data

Alsadat,

Nagarjuna,

Hassan

et al. 2023

AIP Advances

View full text Add to dashboard Cite

show abstract

A New Three-Parameter Paralogistic Distribution: Its Properties and Application

Idemudia¹,

Ekhosuehi²

2021

Journal of Data Science

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In this paper, we considered a new generalization of the paralogistic distribution which we called the three-parameter paralogistic distribution. Some properties of the new distribution which includes the survival function, hazard function, quantile function, moments, Renyi entropy and the maximum likelihood estimation (MLE) of its parameters are obtained. A simulation study shows that the MLE of the parameters of the new distribution is consistent and asymptotically unbiased. An applicability of the new three-parameter paralogistic distribution was subject to a real lifetime data set alongside with some related existing distributions such as the Paralogistic, Gamma, Transformed Beta, Log-logistic and Inverse paralogistic distributions. The results obtained show that the new three-parameter paralogistic distribution was superior to other aforementioned distributions in terms of the Akaike information criterion (AIC) and K-S Statistic values. This claim was further supported by investigating the density plots, P-P plots and Q-Q plots of the distributions for the data set under study.

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The Generalized Weibull-Burr Xii Distribution and Its Applications

Cited by 2 publications

References 27 publications

Marshall–Olkin Weibull–Burr XII distribution with application to physics data

Marshall–Olkin Weibull–Burr XII distribution with application to physics data

A New Three-Parameter Paralogistic Distribution: Its Properties and Application

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