Extrapolation Error
نویسنده
چکیده
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 curve-fitting examples when the curves are fitted in one domain and used for prediction in another, which might be referred to as an error of extrapolation. 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 past success in prediction is shown to perform better in some simulated examples. This paper formulates the 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.
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