Please use this identifier to cite or link to this item: http://ir.library.ui.edu.ng/handle/123456789/5325
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dc.contributor.authorObisesan, K. O.-
dc.contributor.authorUdomboso, C. G.-
dc.contributor.authorOsowole, O. I.-
dc.contributor.authorAlaba, O. O.-
dc.date.accessioned2021-05-25T09:22:37Z-
dc.date.available2021-05-25T09:22:37Z-
dc.date.issued2008-
dc.identifier.otherui_art_obisesan_on_2008-
dc.identifier.otherWest African Journal of Biophysics and Biomathematics 1, pp. 33-45-
dc.identifier.urihttp://ir.library.ui.edu.ng/handle/123456789/5325-
dc.description.abstractWe consider maximum likelihood estimation logarithmic transformation irrespective of mass of density functions. The estimators are assumed to be consistent, convergent and existing. They are referred to as asymptotically minimum-variance sufficient unbiased estimators (AMVSU). We find that the likelihood function gives accurate result when maximized than the log-likelihood. This is because logarithmic transformation has potential problems. We consider a uniform case where the parameter 0 cannot be estimated by calculus but order-statistics. We fit a truncated Poison distribution into data on damaged done after estimating λ by a Newton-Raphson Iterative Algorithm.en_US
dc.language.isoenen_US
dc.titleOn the maximization of the likelihood function against Iogarithmic transformationen_US
dc.typeArticleen_US
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