Stats, Volume 4, Issue 3
2021 September - 13 articles
Cover Story: High-dimensional classification studies have become widespread across various domains. In this paper, we propose a robust and sparse estimator for logistic regression models that simultaneously tackles the presence of outliers and/or irrelevant features. We rely on L0-constraints and mixed-integer conic programming techniques to solve the underlying double combinatorial problem in a framework that allows one to pursue optimality guarantees. Our proposal is used to investigate the main drivers of honeybee (Apis mellifera) loss in Pennsylvania through annual winter loss survey data, where it produces a more interpretable classification model and provides evidence for several outlying observations. In addition, numerical simulations show that our approach outperforms other methods across most performance measures in the considered settings. View this paper
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Stats - ISSN 2571-905X