Bayesian MI-LASSO for Variable Selection on Multiply-Imputed Data: Application to an Environmental Health Survey

Thomas M. Menino Convention & Exhibition Center 

Multiple imputation is widely used for handling missing data in surveys. For variable selection on multiply-imputed datasets, however, if selection is performed on each imputed dataset separately, it can result in different sets of selected variables across datasets. MI-LASSO, one of the most commonly used approaches to this problem, regards the same variable across all separate imputed datasets as a group variable and exploits the group LASSO to yield a consistent variable selection across all the multiply-imputed datasets. In this paper, we extend MI-LASSO to a Bayesian framework and propose four Bayesian MI-LASSO models for variable selection on multiply-imputed data, including three shrinkage prior-based and one Spike-and-Slab prior-based methods. To overcome the limitations of traditional, threshold-based model selection, we further develop a principled four-step projection predictive procedure that provides robust variable selection and facilitates coherent post-selection inference. Simulation studies showed that the Bayesian MI-LASSO models outperformed MI-LASSO and other alternative approaches, achieving higher specificity and lower mean squared error across a range of settings. We illustrated these methods through an environmental health survey application using a multiply-imputed dataset from the University of Michigan Dioxin Exposure Study. The R package BMIselect is available on CRAN.

Bayesian models

Environmental health survey

Group LASSO

Multiple imputation

Projection predictive variable selection