On Apr 2, 11:09 am, Ray Koopman <koop...@[EMAIL PROTECTED]
> wrote:
> On Apr 2, 11:57 am, Luna Moon <lunamoonm...@[EMAIL PROTECTED]
> wrote:
>
>
>
> > On Apr 1, 10:57 pm, Ray Koopman <koop...@[EMAIL PROTECTED]
> wrote:
> >> On Apr 1, 10:13 pm, Luna Moon <lunamoonm...@[EMAIL PROTECTED]
> wrote:
>
> >>> Hi all,
>
> >>> Suppose I have a model and I've used MLE to estimate the parameters
> >>> for the model. What are the good methods that I can use the test the
> >>> goodness of the MLE estimation results?
>
> >>> Thanks!
>
> >> If your model is sufficiently close to correct then the inverse
> >> of the matrix of second derivatives of the negative log likelihood,
> >> evaluated at the likelihood-maximizing estimates, is usually a
> >> consistent estimate of the covariance matrix of the estimates.
> >> More generally, you can always get an empirical covariance matrix
> >> by bootstrapping or jackknifing.
>
> > How does that relate to evaluation of the performance?
>
> The same way that the standard error of any estimator relates to its
> performance. What do you mean by "evaluation of the performance"?
How about model mis-specification?
Thanks!
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