Transmuted Weibull distribution and its applications

Pobocikova, Ivana; Michalková, Mária

doi:10.1051/matecconf/201815708007

Cited by 14 publications

(9 citation statements)

References 13 publications

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“…The transmuted generalized linear exponential distribution was introduced by Elbatal et al [ 7 ]; among others. Poboková and Michalková [ 8 ] proposed a transmuted Weibull distribution. Ali and Athar [ 9 ] have created a new generalized transmuted family of distributions (TD).…”

Section: Introductionmentioning

confidence: 99%

A New Transmuted Generalized Lomax Distribution: Properties and Applications to COVID‐19 Data

Azm

Almetwally

Alaziz

et al. 2021

Computational Intelligence and Neuroscience

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A new five-parameter transmuted generalization of the Lomax distribution (TGL) is introduced in this study which is more flexible than current distributions and has become the latest distribution theory trend. Transmuted generalization of Lomax distribution is the name given to the new model. This model includes some previously unknown distributions. The proposed distribution's structural features, closed forms for an rth moment and incomplete moments, quantile, and Rényi entropy, among other things, are deduced. Maximum likelihood estimate based on complete and Type-II censored data is used to derive the new distribution's parameter estimators. The percentile bootstrap and bootstrap-t confidence intervals for unknown parameters are introduced. Monte Carlo simulation research is discussed in order to estimate the characteristics of the proposed distribution using point and interval estimation. Other competitive models are compared to a novel TGL. The utility of the new model is demonstrated using two COVID-19 real-world data sets from France and the United Kingdom.

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Section: Introductionmentioning

confidence: 99%

A New Transmuted Generalized Lomax Distribution: Properties and Applications to COVID‐19 Data

Azm

Almetwally

Alaziz

et al. 2021

Computational Intelligence and Neuroscience

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show abstract

“…ii) Cubic ranked map Transmuted Weibull distribution (TWD3) Using ( 29) in to ( 16); ( 29) and ( 30) into (17) to get the new Cubic map ranked transmuted Weibull cdf as…”

Section: Transmuted Weibull Distributionmentioning

confidence: 99%

Generalized Rank Mapped Transmuted Distribution for Generating Families of Continuous Distributions

Ali¹,

Athar²

2021

JSTA

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This study introduces generalized transmuted family of distributions. We investigate the special cases of our generalized transmuted distribution to match with some other generalization available in literature. The transmuted distributions are applied to Weibull distribution to find generalized rank map transmuted Weibull distribution. The distributional characteristics such as probability curve, mean, variance, skewness, kurtosis, distribution of largest order statistics, and their characteristics studied to compare with ordinary Weibull distribution. Hazard rate functions and distributional characteristics of largest order statistics of transmuted distributions are also studied. It is observed that the transmuted distributions are more flexible to model real data, since the data can present a high degree of skewness and kurtosis. If someone is interested to locate more flexible and higher degree of skewed distribution can explore this generalized transmuted family of distributions for future use.

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“…Therefore, researchers in the last few years developed various extensions and modified forms of the Weibull distribution to obtain more flexible distributions for modeling experimental data. For example Ahmad et al [1] have discussed generalized form of new Extended Weibull distribution, Pobocikova et al [17] have introduced transmuted Weibull distribution along with its applications. Cordeiro et al [6], [7], has introduced some other form of generalization of Weibull distribution such as Lindly Weibull distribution and Transmuted Generalized Modified Weibull distribution and Gumbel Weibull distribution (Al-Aqtash et al [2]).…”

Section: Introductionmentioning

confidence: 99%

Some Properties on Fréchet-Weibull Distribution with Application to Real Life Data

Deka¹,

Das²,

Baruah³

2021

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Research, development and extensive use of generalized form of distributions in order to analyze and modeling of applied sciences research data has been growing tremendously. Weibull and Fréchet distribution are widely discussed for reliability and survival analysis using experimental data from physical, chemical, environmental and engineering sciences. Both the distributions are applicable to extreme value theory as well as small and large data sets. Recently researchers develop several probability distributions to model experimental data as these parent models are not adequate to fit in some experiments. Modified forms of the Weibull distribution and Fréchet distribution are more flexible distributions for modeling experimental data. This article aims to introduce a generalize form of Weibull distribution known as Fréchet-Weibull Distribution (FWD) by using the T-X family which extends a more flexible distribution for modeling experimental data. Here the pdf and cdf with survival function [S(t)], hazard rate function [h(t)] and asymptotic behaviour of pdf and survival function and the possible shapes of pdf, cdf, S(t) and h(t) of FWD have been studied and the parameters are estimated using maximum livelihood method (MLM). Some statistical properties of FWD such as mode, moments, skewness, kurtosis, variation, quantile function, moment generating function, characteristic function and entropies are investigated. Finally the FWD has been applied to two sets of observations from mechanical engineering and shows the superiority of FWD over other related distributions. This study will provide a useful tool to analyze and modeling of datasets in Mechanical Engineering sciences and other related field.

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Transmuted Weibull distribution and its applications

Abstract: In this paper we study new distribution called transmuted Weibull distribution. Some properties of this distribution are described. The usefulness of the distribution for modelling data is illustrated using real data set.

Cited by 14 publications

References 13 publications

A New Transmuted Generalized Lomax Distribution: Properties and Applications to COVID‐19 Data

A New Transmuted Generalized Lomax Distribution: Properties and Applications to COVID‐19 Data

Generalized Rank Mapped Transmuted Distribution for Generating Families of Continuous Distributions

Some Properties on Fréchet-Weibull Distribution with Application to Real Life Data

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