奇异模型和有限样本量的基于信息的推断：一个频率论信息准则
Information-based inference for singular models and finite sample sizes: A frequentist information criterion
摘要：
在基于信息的推理范例中，通过选择具有最佳估计预测性能的候选模型来执行模型选择。这种方法的成功取决于预测复杂性估计的准确性。在常规模型的大样本容量限制下，预测性能可以通过赤池信息准则（AIC）得到很好的估计。然而，这种近似可以显著低估或高估的复杂性，在广泛的重要应用中，模型是非正规的或有限样本大小的校正是显着的。我们引入了一个改进的近似的复杂性，用于定义一个新的信息准则：频率信息准则（QIC）。QIC将基于信息的推理的适用性扩展到有限样本大小的规则模型和奇异模型。我们证明了权力和比较优势的QIC在一些例子分析。
英文摘要：
In the information-based paradigm of inference, model selection is performed by selecting the candidate model with the best estimated predictive performance. The success of this approach depends on the accuracy of the estimate of the predictive complexity. In the large-sample-size limit of a regular model, the predictive performance is well estimated by the Akaike Information Criterion (AIC). However, this approximation can either significantly under or over-estimating the complexity in a wide range of important applications where models are either non-regular or finite-sample-size corrections are significant. We introduce an improved approximation for the complexity that is used to define a new information criterion: the Frequentist Information Criterion (QIC). QIC extends the applicability of information-based inference to the finite-sample-size regime of regular models and to singular models. We demonstrate the power and the comparative advantage of QIC in a number of example analyses.
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