"Herman Rubin" <[EMAIL PROTECTED]> wrote in message
9vqoln$[EMAIL PROTECTED]">news:9vqoln$[EMAIL PROTECTED]...
>
> Maximum likelihood is ASYMPTOTICALLY optimal in LARGE
> samples. It may not be good for small samples; it pays
> to look at how the actual likelihood function behaves.
> The fit is always going to improve with more parameters.
>
This may be the trouble in the actual problem being attempted, but
there are other possibilities, besides the potential for having
programmed things incorrectly. One such trouble might be that the
parameters are constrained and that the maximum-likelihood estimates
given such constraints are falling on the edge of the allowed region
.. then the usual asymptotics don't apply.
David Jones
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