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    On the maximization of the likelihood function against Iogarithmic transformation
    (2008) Obisesan, K. O.; Udomboso, C. G.; Osowole, O. I.; Alaba, O. O.
    We 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.