Model comparisons and model selections based on generalization criterion methodology
Model comparisons and model selections based on generalization criterion methodology
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DOI：
10.1006/jmps.1999.1282
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发表时间：
2000-03-01
影响因子：
1.8
通讯作者：
Wang, YM
中科院分区：
文献类型：
--
作者：
Busemeyer, JR;Wang, YM
本文的目的是形式化模型比较的泛化准则方法。该方法有潜力为复杂和非嵌套模型提供强大的比较，这些模型也可能在参数数量方面有所不同。在以下关键程序中，泛化标准与更广为人知的交叉验证标准不同。虽然两者都采用校准阶段来估计参数，但交叉验证在验证阶段采用来自相同设计的复制样本，而泛化在关键阶段采用新设计。提出了两个泛化标准方法的例子，证明了它在从一组模型中选择基于合理科学原则的模型时的有用性，这些模型也包含缺乏合理科学原则的模型，这些模型要么过于复杂，要么过于简化。泛化准则的主要优点是它依赖于对新条件的外推。毕竟，对新情况的准确的先验预测是一个好的科学理论的标志。(C) 2000年学术出版社。
The purpose of this article is to formalize the generalization criterion method for model comparison. The method has the potential to provide powerful comparisons of complex and nonnested models that may also differ in terms of numbers of parameters. The generalization criterion differs From the better known cross-validation criterion in the following critical procedure. Although both employ a calibration stage to estimate parameters, cross-validation employs a replication sample from the same design for the validation stage, whereas generalization employs a new design for the critical stage. Two examples of the generalization criterion method are presented that demonstrate its usefulness for selecting a model based on sound scientific principles out of a set that also contains models lacking sound scientific principles that are either overly complex or oversimplified. The main advantage of the generalization criterion is its reliance on extrapolations to new conditions. After all, accurate a priori predictions to new conditions are the hallmark of a good scientific theory. (C) 2000 Academic Press.