Information-theoretic approaches to model selection, such as Akaike's information criterion (AIC) and cross validation, provide a rigorous framework to select among candidate hypotheses in ecology, yet the persistent concern of overfitting undermines the interpretation of inferred processes. A common misconception is that overfitting is due to the choice of criterion or model score, despite research demonstrating that selection uncertainty associated with score estimation is the predominant influence. Here we introduce a novel selection rule that identifies a parsimonious model by directly accounting for estimation uncertainty, while still retaining an information-theoretic interpretation. The new rule, which is a modification of the existing one-standard-error rule, mitigates overfitting and reduces the likelihood that spurious effects will be included in the selected model, thereby improving its inferential properties. We present the rule and illustrative examples in the context of maximum-likelihood estimation and Kullback-Leibler discrepancy, although the rule is applicable in a more general setting, including Bayesian model selection and other types of discrepancy.

中文翻译：

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使用信息论的简约模型选择：修改后的选择规则

模型选择的信息理论方法，例如 Akaike 的信息标准 (AIC) 和交叉验证，提供了一个严格的框架来在生态学中的候选假设中进行选择，但过度拟合的持续关注破坏了对推断过程的解释。一个常见的误解是过度拟合是由于标准或模型分数的选择，尽管研究表明与分数估计相关的选择不确定性是主要影响因素。在这里，我们引入了一种新颖的选择规则，该规则通过直接考虑估计不确定性来识别简约模型，同时仍保留信息理论解释。新规则是对现有单标准错误规则的修改，减轻过度拟合并降低所选模型中包含虚假效应的可能性，从而改善其推理特性。我们在最大似然估计和 Kullback-Leibler 差异的背景下呈现规则和说明性示例，尽管该规则适用于更一般的设置，包括贝叶斯模型选择和其他类型的差异。