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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.
STA: 332 Laboratory/fieldwork on experimental design I
(Distance Learning Centre, University of Ibadan, 2007) Udomboso, C. G.
Statistical computing: STA 231
(Distance Learning Centre, University of Ibadan, 2007) Udomboso, C. G.