ArticleStatistics in medicine2025
Robust Transfer Learning for High-Dimensional GLM Using
Article in Statistics in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
In the analysis of complex diseases, high-dimensional profiling data is important for assessing risks and detecting biomarkers. With the increasing accessibility of cancer genomic data, the sample sizes remain limited in most studies. Hence, borrowing information from additional data sources is thus desirable to improve estimation and prediction. Transfer learning has been demonstrated to be flexible and effective in boosting modeling performance with a record in biomedical applications. In practice, outliers and even data contamination often occur. However, existing transfer learning methods often lack robustness to outliers and data contamination, issues commonly observed in real-world biomedical data. In this study, we propose a robust transfer learning approach based on the minimum
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