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"?
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