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How does AIC differ from cross validation?
What follows, AIC accounts for uncertainty in the data (-2LL) and makes the assumption that more parameters leads to higher risk of overfitting (2k). Cross-validation just looks at the test set performance of the model, with no further assumptions.
What does cross validation mean in statistics?
Cross-validation, sometimes called rotation estimation or out-of-sample testing, is any of various similar model validation techniques for assessing how the results of a statistical analysis will generalize to an independent data set.
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?
How is the Akaike information criterion ( AIC ) used?
The Akaike information criterion (AIC) is an estimator for out-of-sample deviance and thereby relative quality of statistical models for a given set of data. Given a collection of models for the data, AIC estimates the quality of each model, relative to each of the other models. Thus, AIC provides a means for model selection.
Why is overfitting classified as having lower AIC?
This leads to contradictory results with overfitting being classified as having lower AIC. A better procedure is to calculate the noise level independently, and accept the model that most exactly duplicates that noise level.
How does AIC work in estimating the amount of information lost?
In estimating the amount of information lost by a model, AIC deals with the trade-off between the goodness of fit of the model and the simplicity of the model. In other words, AIC deals with both the risk of overfitting and the risk of underfitting.