Re: fminserch does not converge for every variable

yaoli <yaoli54@xxxxxxxxx> wrote in message
<260070.1214846233318.JavaMail.jakarta@xxxxxxxxxxxxxxxxxxxxxx>...
Hi,

I'm doing a longitudinal simulation study to check if my modeling approach
could give the correct parameter estimate. To do this, I use fminsearch
function to find the MLE.

The code is:
options = optimset('MaxFunEvals',100000,'MaxIter',100000,'TolFun',1.e-
6,'TolX', 1.e-6);
[x0new,fval]=fminsearch(@loglike,x0,options,Y,t);

where X0 is a vector of 17 parameters' true values and t is the time points.

The problem is, for different generated data, either 14 parameter estimates
could be correctly found whereas the other 3 (always the same 3) could not
be, or the algorithm can not converge.

So could it because that my likelihood function is intrinsically too hard to
be handled by simplex?

Thanks in advance!

yaoli

Returning to the original question, it is not at all
uncommon that an optimizer reaches a different
solution when you start from a different point.
And when you have that many parameters, it is
also common to have multiple local solutions.
Your friends may not even know that they have
not truly found the global solution when they
have solved their own problems, at least if they
have similar objective functions to you.

Finally, if you really have discrete parameters,
then fminsearch is not at all applicable to this
problem.

John
.