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The exponentiated generalized power series: family of distributions: theory, properties and applications

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dc.contributor.author Oluyede, Broderick
dc.contributor.author Mashabe, Baitshephi
dc.contributor.author Fabgamigbe, Adeniyi Francis
dc.contributor.author Makubate, Boikanyo
dc.contributor.author Wanduku, Divine
dc.date.accessioned 2021-09-01T15:10:16Z
dc.date.available 2021-09-01T15:10:16Z
dc.date.issued 2020-08-24
dc.identifier.citation Oluyede, B. O. et al. (2020) The exponentiated generalized power series: Family of distributions: theory, properties and applications. Heliyon, 6(8), [e04653]. https://doi.org/10.1016/j.heliyon.2020.e04653 en_US
dc.identifier.issn 24058440
dc.identifier.uri http://repository.biust.ac.bw/handle/123456789/337
dc.description.abstract We propose a new generalized family of distributions called the exponentiated generalized power series (EGPS) family of distributions and study its sub-model, the exponentiated generalized logarithmic (EGL) class of distributions, in detail. The structural properties of the new model (EGPS) and its sub-model (EGL) distribution including moments, order statistics, Rényi entropy, and maximum likelihood estimates are derived. We used the method of maximum likelihood to estimate the parameters of this new family of distributions. Simulation study was carried out to examine the bias and the mean square error of the maximum likelihood estimators for each of the model's parameters. Finally, we showed real life data examples to illustrate the models' applicability, flexibility and usefulness. en_US
dc.description.sponsorship en_US
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.subject Mathematics en_US
dc.subject Statistics en_US
dc.subject Exponentiated distribution en_US
dc.subject Maximum likelihood estimation en_US
dc.subject Logarithmic distribution en_US
dc.title The exponentiated generalized power series: family of distributions: theory, properties and applications en_US
dc.description.level phd en_US
dc.description.accessibility unrestricted en_US
dc.description.department mss en_US


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