A Simple Aggregation Rule for Penalized Regression Coefficients after Multiple Imputation

Journal of Data Science

article

Ryan A. Peterson¹ 

[1] Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado-Denver Anschutz Medical Campus

关键词: elastic net;    LASSO;    minimax concave penalty;    missing data;    regularization;   

DOI  :  10.6339/21-JDS995

学科分类：土木及结构工程学

来源: JDS

【 摘 要 】

Early in the course of the pandemic in Colorado, researchers wished to fit a sparse predictive model to intubation status for newly admitted patients. Unfortunately, the training data had considerable missingness which complicated the modeling process. I developed a quick solution to this problem: Median Aggregation of penaLized Coefficients after Multiple imputation (MALCoM). This fast, simple solution proved successful on a prospective validation set. In this manuscript, I show how MALCoM performs comparably to a popular alternative (MI-lasso), and can be implemented in more general penalized regression settings. A simulation study and application to local COVID-19 data is included.

【 授权许可】

CC BY