From: Annarita Roscino (firstname.lastname@example.org)
Date: Tue 23 Mar 2004 - 23:39:07 EST
I am trying to maximize a complex log likelihood function with respect to 10
parameters using the method "L-BFGS-B" implemented in the optim procedure .
The algorithm that I have written always converges, but I have got different
solutions running the algorithm many times on the same dataset. I suspect
that the log likelihood is flat....
The results usually differ of a quantity that is small (+- 0.04). But,
that some of the parameters can vary from -1 to 1, I am not satisfied with
the variability in the solutions and I do not know how to choose a solution
among the results that I get.
I have tried to iterate the optim procedure, using as initial values the
results of the previous step to see if the algorithm does rich the
convergence.But it does not, after 1000 iterations.
I would like to summarise the results of the maximization procedure at the
different iterations as an estimator of the unknown parameters, for
instance, as a kind of MC average.
Does anybody have any expercience in such a theme?
I would really appreciate comments or ideas! It is very important!
Department of Statistical Sciences
University of Bari
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