On Tue, 16 Nov 2004 [EMAIL PROTECTED] wrote:
we are a pool of phd students and we have a question about AIC. We are interested in calculating the AIC for a mixture model on galaxies data. So far we have found AIC works only for regression models, whereas we need AIC for a mixture of Normal with mean, sd and weights given by our EM algorithm.
What is the problem? The concept AIC or the function AIC()? If the concept, it was originally developed in the area of AR(p) time series models, using asymptotic theory for MLEs, and you need to check that the concepts apply to your situation (the original papers do not).
Note there are some fundamental problems here: the likelihood for a mixture of normals is unbounded, the MLE corresponding to degenerate (zero-variance) components. And AIC is about maximized likelihoods, with maxima in the interior of the space. So you need to take some professional statistical advice about the concepts.
-- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595
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