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Gaussian scale mixture seems to be a good tool for modeling heavy-tailed residual in regression. I wonder if there is a systematic way to fit Gaussian scale mixture to data ? It appears that expectation-maximization algorithm only works for fitting location-scale mixture distribution, is that correct? |
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Here's an answer to a slightly different question. There exists ways of more directly defining a heavy tailed distribution other than using a mixture of gaussians (which are inherently not heavy-tailed). The challenge in the past has been efficient/compact representation, as well as learning/inference. There has been some work recently to address these issues, e.g., http://www.select.cs.cmu.edu/publications/paperdir/nips10-bickson-guestrin.pdf This might better solve whatever problem it is you have. Thanks for your response. But the class of Gaussian scale mixture distributions contains many widely used heavy-tail distributions, e.g., the symmetric stable distributions and Laplace distributions. In fact, in the work you mentioned, "Inference with Multivariate Heavy-Tails in Linear Models", the authors assume that all observation nodes of their model are distributed according to a stable distribution. So intuitively, I think the class of Gaussian scale mixture would offer better fitting flexibility, although the learning/inference may be much harder.
(Aug 20 '11 at 15:20)
Li Tang
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