标题： 基于最小描述长度的模型选择：Fisher信息近似的小样本量下界 显示英文标题

标题： Model selection by minimum description length: Lower-bound sample sizes for the Fisher information approximation

摘要： Fisher信息近似（FIA）是模型选择中最小描述长度原则的一种实现。与AIC或BIC等信息准则不同，它具有考虑模型函数形式的优势。不幸的是，在有限样本情况下，FIA可能会产生误导，导致最坏情况下竞争模型复杂性项的正确秩顺序发生反转。作为补救措施，我们提出了一种样本大小的下界 $N'$ ，足以排除此类错误。我们通过来自多分类处理树模型族的三个例子来说明这种方法。 显示英文摘要

摘要： The Fisher information approximation (FIA) is an implementation of the minimum description length principle for model selection. Unlike information criteria such as AIC or BIC, it has the advantage of taking the functional form of a model into account. Unfortunately, FIA can be misleading in finite samples, resulting in an inversion of the correct rank order of complexity terms for competing models in the worst case. As a remedy, we propose a lower-bound $N'$ for the sample size that suffices to preclude such errors. We illustrate the approach using three examples from the family of multinomial processing tree models.