Flexible variable selection in the presence of missing data.

ABSTRACT: In many applications, it is of interest to identify a parsimonious set of features, or panel, from multiple candidates that achieves a desired level of performance in predicting a response. This task is often complicated in practice by missing data arising from the sampling design or other random mechanisms. Most recent work on variable selection in missing data contexts relies in some part on a finite-dimensional statistical model, e.g., a generalized or penalized linear model. In cases where this model is misspecified, the selected variables may not all be truly scientifically relevant and can result in panels with suboptimal classification performance. To address this limitation, we propose a nonparametric variable selection algorithm combined with multiple imputation to develop flexibl

SUBMITTER: Williamson BD 

PROVIDER: S-EPMC11323294 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature