This paper analyses straightforward Bonferroni adjustments to critical values of in-sample tests of predictability, when data mining is used to search across models. Unlike conventional pseudo-out-of-sample tests, these in-sample tests have stable family-wise error rates (FWERs) when searching for models that predict well. Furthermore, when data mining, these in-sample tests have more power than pseudo-out-of-sample tests for identifying true predictability.

中文翻译：

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可预测性的样本内测试优于伪样本外测试，即使在数据挖掘时也是如此

当数据挖掘用于跨模型搜索时，本文分析了对可预测性样本内测试临界值的直接 Bonferroni 调整。与传统的伪样本外测试不同，这些样本内测试在搜索预测良好的模型时具有稳定的全族错误率 (FWER)。此外，在数据挖掘时，这些样本内测试比伪样本外测试更能识别真实的可预测性。