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st: Why does specifying "ml search" results in different estimates?
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Subject
st: Why does specifying "ml search" results in different estimates?
Date
Wed, 10 Apr 2013 05:07:50 +0100
Dear Statalists,
I’m compiling a model by using the command, “ml”, and the method is
“d0”. (I don’t have analytical formulae of the score and hessian, so I
can’t use “d1” or “d2”. The method “lf” is not suitable for my
model.)
When maximising the log-likelihood, I found that specifying “ml search”
resulted in different estimates, and the differences of some estimates were
quite obvious. I’ve tried to specify more rigorous convergence criteria,
but that didn’t solve the problem. Hence I’m seeking your advice. What
would be the causes of this problem? How to solve/minimise it? Is it always
better to use “ml search”, if I don’t care about the extra computation
time?
Any suggestions are welcome, and thank you very much in advance.
Kirin
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