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a method of estimating parameters of a statistical model by choosing the parameter value that optimizes the probability of observing the given sample.

15 votes
2 answers
360 views

For what models does the bias of MLE fall faster than the variance?

Let $\hat\theta$ be a maximum likelihood estimate of a true parameter $\theta^*$ of some model. As the number of data points $n$ increases, the error $\lVert\hat\theta-\theta^*\rVert$ typically decre …
Mike Izbicki's user avatar
2 votes
2 answers
4k views

Central limit theorem for maximum likelihood estimators when modelling assumptions are violated

Lehman's Element's of Statistical Learning Theory gives in Theorem 7.5.2 a central limit theorem for multiparamter maximum likelihood estimators. (Many other sources provide similar theorems.) The t …
Mike Izbicki's user avatar