Standardization of continuous and categorical covariates in sparse penalized regressions

In sparse penalized regressions, candidate covariates of different units need to be standardized beforehand so that the coefficient sizes are directly comparable and reflect their relative impacts, which leads to fairer variable selection. However, when covariates of mixed data types (e.g. continuous, binary or categorical) exist in the same dataset, the commonly used standardization methods may lead to different selection probabilities even when the covariates have the same impact on or level of association with the outcome. In this paper, we propose a novel standardization method that targets at generating comparable selection probabilities in sparse penalized regressions for continuous, binary or categorical covariates with the same impact. We illustrate the advantages of the proposed method in simulation studies, and apply it to the National Ambulatory Medical Care Survey data to select factors related to the opioid prescription in the US.

Automated summary

This paper proposes a novel standardization method for different data types of covariates in sparse penalized regressions, aiming to solve the problem of different selection probabilities caused by traditional methods. The authors illustrate the advantages of the proposed method through simulation studies and empirical analysis.