[R] log likelihood maximization with optim

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From: Annarita Roscino (aroscino@dss.uniba.it)
Date: Tue 23 Mar 2004 - 23:39:07 EST


Message-id: <000801c410d3$d850e2e0$08befea9@lab1>


Dear all.
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,
given
 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!
 Many thanks,
Annarita

 Annarita Roscino
Department of Statistical Sciences
University of Bari
 tel. 00390805049353
email: aroscino@dss.uniba.it

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