AIC is a popular model comparison measure (despite of its potential shortcomings).

I am wondering whether it is legit to compare AIC (or Akaike weights) for models that were fitted without requiring maximum likelihood. Consider e.g. that the EM algorithm for fitting Gaussian mixtures was terminated prematurely.

The R help page for AIC states: "... whereas AIC can be computed for models not fitted by maximum likelihood, their AIC values should not be compared."

Are there any other measures that can be used in this scenario?

Any references to papers/books that argue for it or against will be greatly appreciated.

  • 2
    $\begingroup$ The EM algorithm is a method of obtaining maximum likelihood estimates. i would say that you can use AIC in that case as well. $\endgroup$ – JohnK Dec 24 '15 at 10:01
  • $\begingroup$ Thanks for your reply @JohnK. Just to be clear, even if the EM is terminated before it converges to MLE, is it valid to calculate AIC and use it to compare models, which might have reached MLE or not? $\endgroup$ – DataD'oh Dec 24 '15 at 15:09

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