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When should I use AICc?
As a rule of thumb, always using AICc is safest, but AICc should especially be used when the ratio of your data points (n) : # of parameters (k) is < 40….AIC makes assumptions that you:
- Are using the same data between models.
- Are measuring the same outcome variable between models.
- Have a sample of infinite size.
What value of AIC is good?
The AIC function is 2K – 2(log-likelihood). Lower AIC values indicate a better-fit model, and a model with a delta-AIC (the difference between the two AIC values being compared) of more than -2 is considered significantly better than the model it is being compared to.
Can AIC value be negative?
The absolute values of the AIC scores do not matter. These scores can be negative or positive. In your example, the model with AIC=−237.847 is preferred over the model with AIC=−201.928. You should not care for the absolute values and the sign of AIC scores when comparing models.
When to use AICC instead of AIC for model comparison?
I know that when the number of observations is small (one paper says <40 times the number of parameters), AICc should be used instead of AIC for model comparison. Does this imply that when the number of observations is large, AIC should be used rather than AICc?
Which is model should be chosen using AICC / BIC and p-value?
The AIC, the BIC and the p -values all address different questions. For feature selection (variable selection, model selection), only the former two are relevant. See e.g. Rob J. Hyndman’s blog posts “Statistical tests for variable selection” and “Facts and fallacies of the AIC”.
Which is better AIC or p-value for prediction?
For a prediction situation AIC is more relevant than BIC or p-values. However, to pick a single model on the basis of any of these and then to analyze the data as if no model selection had been done, is likely inappropriate (unless one candidate model is so vastly better than the others as to remove all model uncertainty).
What is the normal distribution of AIC and Bic?
An observation of both the x and the z covariates is generated simultaneously via a multivariate normal distribution (MVN) as at (4), to allow for potential correlations between the covariates; we define means (,) and a covariance matrix () for the MVN. The residual r has a normal distribution centred on 0 with variance (Table 1 ).