Combining Robust and Traditional Least Squares Methods: A Critical Evaluation

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.