Apologies for cross postings.
Dear all.
I am trying to maximize a complex log likelihood function with respect to 10
parameters with R. I am using the optimization procedure optim() and the
method "L-BFGS-B" which allows box constraints.
The algorithm that I have written always converges, but I have got different
solutions running the algorithm many times on the same dataset. I have the
impression that the log likelihood is flat....The results usually differ of
a quantity that is small (+- 0.04). But,given that some of the parameters
can vary from -1 to 1, I am not satisfied with the uncertainty 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!
Many thanks,
Annarita
Annarita Roscino
Department of Statistical Sciences
University of Bari
ph. 00390805049353
email: [log in to unmask]
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