A Comparison of Alternative Models for the Demand for Medical Care
利用实验数据比较不同医疗需求模型，发现更符合分布假设并使用非参数重转换因子的模型预测误差更小，而简单模型因对异常值不稳健或假设违背而不一致。
We have tested alternative models of the demand for medical care using experimental data. The estimated response of demand to insurance plan is sensitive to the model used. We therefore use a split-sample analysis and find that a model that more closely approximates distributional assumptions and uses a nonparametric retransformation factor performs better in terms of mean squared forecast error. Simpler models are inferior either because they are not robust to outliers (e.g., ANOVA, ANOCOVA), or because they are inconsistent when strong distributional assumptions are violated (e.g., a two-parameter Box-Cox transformation).