Simultaneous feature selection and outlier detection with optimality guarantees.

ABSTRACT: Biomedical research is increasingly data rich, with studies comprising ever growing numbers of features. The larger a study, the higher the likelihood that a substantial portion of the features may be redundant and/or contain contamination (outlying values). This poses serious challenges, which are exacerbated in cases where the sample sizes are relatively small. Effective and efficient approaches to perform sparse estimation in the presence of outliers are critical for these studies, and have received considerable attention in the last decade. We contribute to this area considering high-dimensional regressions contaminated by multiple mean-shift outliers affecting both the response and the design matrix. We develop a general framework and use mixed-integer programming to simultaneously pe

SUBMITTER: Insolia L 

PROVIDER: S-EPMC10286774 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature