LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

Standardization of continuous and categorical covariates in sparse penalized regressions

Photo by roberto_sorin from unsplash

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… Click to show full abstract

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.

Keywords: covariates sparse; standardization continuous; sparse penalized; categorical covariates; penalized regressions; continuous categorical

Journal Title: Statistical Methods in Medical Research
Year Published: 2022

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.