On the maximization of the likelihood function against Iogarithmic transformation (2008)
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.