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The burr-weibull power series class of distributions

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dc.contributor.author Oluyede, Broderick
dc.contributor.author Mdlongwa, Precious
dc.contributor.author Makubate, Boikanyo
dc.contributor.author Huang, Shujiao
dc.date.accessioned 2022-06-30T08:26:26Z
dc.date.available 2022-06-30T08:26:26Z
dc.date.issued 2018-12-12
dc.identifier.citation Oluyede, B. et al. (2018) The burr-weibull power series class of distributions, Austrian Journal of Statistics, 48 (1), 1–13. https://doi.org/10.17713/ajs.v48i1.633 en_US
dc.identifier.issn 1026597X
dc.identifier.uri http://repository.biust.ac.bw/handle/123456789/471
dc.description.abstract A new generalized class of distributions called the Burr-Weibull Power Series (BWPS) class of distributions is developed and explored. This class of distributions generalizes the Burr power series and Weibull power series classes of distributions, respectively. A special model of the BWPS class of distributions, the new Burr-Weibull Poisson (BWP) distribution is considered and some of its mathematical properties are obtained. The BWP distribution contains several new and well known sub-models, including Burr-Weibull, Burrexponential Poisson, Burr-exponential, Burr-Rayleigh Poisson, Burr-Rayleigh, Burr-Poisson, Burr, Lomax-exponential Poisson, Lomax-Weibull, Lomax-exponential, Lomax-Rayleigh, Lomax-Poisson, Lomax, Weibull, Rayleigh and exponential distributions. Maximum likelihood estimation technique is used to estimate the model parameters followed by a Monte Carlo simulation study. Finally an application of the BWP model to a real data set is presented to illustrate the usefulness of the proposed class of distributions. en_US
dc.language.iso en en_US
dc.publisher Austrian Statistical Society en_US
dc.subject Burr-weibull distribution en_US
dc.subject Poisson distribution en_US
dc.subject Weibull distribution en_US
dc.subject Burr distribution en_US
dc.subject Maximum likelihood estimation en_US
dc.title The burr-weibull power series class of distributions en_US
dc.description.level phd en_US
dc.description.accessibility unrestricted en_US
dc.description.department mss en_US


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