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On overfitting and post-selection uncertainty assessments (1712.02379v1)
Published 6 Dec 2017 in math.ST, stat.AP, stat.ME, and stat.TH
Abstract: In a regression context, when the relevant subset of explanatory variables is uncertain, it is common to use a data-driven model selection procedure. Classical linear model theory, applied naively to the selected sub-model, may not be valid because it ignores the selected sub-model's dependence on the data. We provide an explanation of this phenomenon, in terms of overfitting, for a class of model selection criteria.
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