The method of maximum likelihood
, as advocated by Fisher in his important papers [9, 10], has become one of the most significant tools for estimation and inference available to statisticians.
The estimation of the parameters of model was done by using method of Maximum Likelihood
. This method deals with finding the estimates of the coefficients which maximize the probability of selecting a sample actually obtained (Kennedy, 2003).
This paper uses the method of maximum likelihood
to estimate the parameters.
However, the operational risk literature reports that attempts to fit operational losses using truncated severity distributions by the method of maximum likelihood
estimation are not always successful.
The parameters of the logit model were estimated using the method of maximum likelihood
, and the significance of the coefficients of the model was evaluated using the Wald test (Everitt, Hothorn 2011; McCullagh, Nelder 1989).
By knowing the density function of this new process we can find discretized likelihood function and use the conventional method of maximum likelihood
estimation to find a closed formula for the parameters.
Estimation of Population Mean by Method of Maximum Likelihood
In many practical applications, parameter estimation for naive Bayes models uses the method of maximum likelihood
; in other words, one can work with the naive Bayes model without believing in Bayesian probability or using any Bayesian methods.