Combining Robust and Traditional Least Squares Methods: A Critical Evaluation
评估了结合稳健与最小二乘方法构建模型的策略，通过实际数据案例揭示其可能导致模型形式错误的风险，并强调对数据生成过程理解的重要性。
The combination of robust and least squares procedures has frequently been recommended as a useful strategy for constructing models. The application of this strategy to a real-world data set resulted in a model with an incorrect functional form. Additional in-depth investigations into the nature of the application, combined with data-error corrections, made possible the construction of a satisfactory model. The results of the modeling activity were evaluated in terms of model face-validity, the predictive performance on a holdout data set, and the ability to meet user requirements. The findings of this study demonstrate the danger of model-form misspecification when one mistakenly assumes that the combination of robust and least squares procedures compensates for a lack of knowledge about the processes underlying the generation of the data.