Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/12568
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dc.contributor.authorArshad, Mohd.en_US
dc.date.accessioned2023-12-14T12:37:39Z-
dc.date.available2023-12-14T12:37:39Z-
dc.date.issued2023-
dc.identifier.citationArshad, M., Pathak, A. K., Azhad, Q. J., & Khetan, M. (2023). Modeling Bivariate Data Using Linear Exponential and Weibull Distributions as Marginals. Mathematica Slovaca. Scopus. https://doi.org/10.1515/ms-2023-0079en_US
dc.identifier.issn0139-9918-
dc.identifier.otherEID(2-s2.0-85168143734)-
dc.identifier.urihttps://doi.org/10.1515/ms-2023-0079-
dc.identifier.urihttps://dspace.iiti.ac.in/handle/123456789/12568-
dc.description.abstractModeling bivariate data with different marginals is an important problem and have numerous applications in diverse disciplines. This paper introduces a new family of bivariate generalized linear exponential Weibull distribution having generalized linear and exponentiated Weibull distributions as marginals. Some important quantities like conditional distributions, conditional moments, product moments and bivariate quantile functions are derived. Concepts of reliability and measures of dependence are also discussed. The methods of maximum likelihood and Bayesian estimation are considered to estimate model parameters. Monte Carlo simulation experiments are performed to demonstrate the performance of the estimators. Finally, a real data application is also discussed to demonstrate the usefulness of the proposed distribution in real-life situations. © 2023 Mathematical Institute Slovak Academy of Sciences.en_US
dc.language.isoenen_US
dc.publisherDe Gruyter Open Ltden_US
dc.sourceMathematica Slovacaen_US
dc.subjectBivariate generalized linear Weibull distributionen_US
dc.subjectgeneralized linear exponential distributionen_US
dc.subjectinferenceen_US
dc.subjectMCMCen_US
dc.subjectmeasures of associationen_US
dc.subjectWeibull distributionen_US
dc.titleModeling Bivariate Data Using Linear Exponential and Weibull Distributions as Marginalsen_US
dc.typeJournal Articleen_US
Appears in Collections:Department of Mathematics

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