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Ruben Roa Ureta wrote:
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| [snip]
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|> ~ not absolutely sure what your question is.
|>
|> ~ If you're talking about evaluating the relative merit of
|> the selected model, it's a question of delta-AIC (or delta-AICc),
|> follow the usual rules of thumb -- <2 is approximately equivalent,
|> |6 is a lot better, >10 is so good that you can probably discard
|> worse models. (See Shane Richards' nice papers on the topic.)
|
| I have traced the rule about 2 as the minimum difference to favour one
| model over the other to remark 2, Ch. 4, Sakamoto, Ishiguro and Kitagawa,
| 1986, Akaike Information Criterion Statistics. D. Re?dle Publishing Co,
| Dordrecht. They use the expression 'significant difference between
| models'. However, they do not explain why they think that 2 is the minimum
| 'significant' delta AIC. Does anybody know more about a justification for
| this threshold?
| Rub?n
~ I would really strongly recommend AGAINST trying to justify
"significance thresholds" for AIC (B&A 2002 say this too).
- -2 AIC points corresponds to adding a single parameter with no
explanatory power at all, so it makes sense to me to consider
this a "minimal change" in the penalized GOF/expected K-L
distance/whatever. You can also consider this in terms
of AIC weights, which in the limit of large sample sizes and
particular (slightly odd) Bayesian priors have an interpretation
in terms of posterior model probabilities.
~ Anyone else?
~ Ben Bolker
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