Cross-Validation and Information Criteria in Causal Modeling
研究了交叉验证和信息准则在市场营销因果模型选择中的有效性，发现样本分割方法对交叉验证效果至关重要，并建议使用Snee的DUPLEX算法；在变量服从多元正态分布时，信息准则优于交叉验证。
Many applications of causal modeling in marketing involve selection among several competing causal models. The author investigates whether common criteria for model selection such as cross-validation indices and information criteria are likely to lead to discovery of the correct population model. Guidance on the use of these selection criteria in practice is provided for substantive marketing researchers. Results indicate that the adequacy of cross-validation depends critically on the method used for sample-splitting. The author suggests the application of Snee's DUPLEX algorithm in this context. For situations in which the assumption of multinormally distributed variables is justified, information criteria are found to be highly appropriate for model selection, outperforming cross-validation methods in several respects.