September 2012
Intermediate to advanced
286 pages
8h 7m
English
Appendix A
Maximum Likelihood Estimation
(Source: Franses and Paap [1])
In the estimation of maximum likelihood, the likelihood function is defined as
(A.1) 
where 
is the joint density distribution for the observed variables 
given 
, and 
summarizes the model parameters 
to 
. The logarithmic likelihood function is
(A.2) 
Since it is not possible to find an analytical solution for the value of 
that maximizes the log-likelihood function, the maximization has to be done using a numerical optimization algorithm. Here, Franses and Paap [1] prefer the Newton–Raphson method, which introduces the gradient ...
