Dear all,
Thanks for the responses to this post. I understand that the topic still requires more research. However, I am a non-statistician in a desperate need to analyze my ecological data with the currently available tools. Please excuse again my non-expert question: Would I commit a huge mistake if I use the likelihood estimates from GLMM as a "good approximate" to the "real" log-likelihood, and therefore calculate AIC from it? Should I use instead any of the existing corrections for AIC? Otherwise, can you suggest any other model selection approach suitable for generalized mixed models?
Nestor
Deepayan Sarkar wrote:
On Sunday 17 April 2005 12:07, Prof Brian Ripley wrote:
On Sun, 17 Apr 2005, Deepayan Sarkar wrote:
[...]
GLMM uses (mostly) the same procedure to get parameter estimates, but as a final step calculates the likelihood for the correct model for those estimates (so the likelihood reported by it should be fairly reliable).
Well, perhaps but I need more convincing. The likelihood involves many high-dimensional non-analytic integrations, so I do not see how GLMM can do those integrals -- it might approximate them, but that would not be `calculates the likelihood for the correct model'. It would be helpful to have a clarification of this claim. (Our experiments show that finding an accurate value of the log-likelihood is difficult and many available pieces of software differ in their values by large amounts.)
You are right, of course. I left out too much trying to be brief (partly because this issue has been discussed before). I'll try to refrain from giving such partial answers in future.
Deepayan
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