Model robustness
Dear all,
I have a question on model robustness; if different sets of initial values
of thetas change the standard errors a lot, should the model be considered
to be too sensitive? I obtain the same estimates for both thetas, omegas and
epsilons regardless of initial values, but the standard errors seems to
increase if one starts with initial values too far away from the minimum.
(Too close seems not to be ideal either.)
Are there any standard routines that one could apply for this kind of
behavior? Could one, for instance, use the mean of the sets of standard
errors from different runs? Or should the model be considered to be unstable
as long as different initial values produce different standard errors? Is
there an acceptable limit; such as when an increase by 25% on all initial
values for theta implies a 20% increase in standard errors but all estimates
remain the same?
All comments are welcome. I have tried to find a similar topic in the
archive but without success.
Best regards,
Martin
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Martin Fransson
Dept. of Computer and Information Science
Linköping University
581 83 LINKÖPING, SWEDEN
<mailto:[EMAIL PROTECTED]> [EMAIL PROTECTED]
+46 13 281467
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