



Extrapolation Error 

This page was last edited on 02/07/02 by Malcolm R Forster 

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Two kinds of error commonly arise in statistical inference. The most common one is sampling error, arising from small samples. The second is the error arising from unrepresentative samples. Such errors occur in curvefitting examples when the curves are fitted in one domain and used for prediction in another. We refer to this error as the extrapolation error. The problem with extrapolation is that standard model selection methods, such as classical hypothesis testing, AIC, BIC, do not correct for extrapolation error. Nevertheless, the simple method of judging models by their success in prediction is shown to perform better in one simulated example. This note formulates the following question in a precise mathematical terms: When does this method work? There is no precise answer to this question at the present time, even in quite simple contexts.

