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September 10, 2025Biometrics

Semi-supervised linear regression: enhancing efficiency and robustness in high dimensions

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Authors

KCKai ChenYZYuqian Zhang

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Overview

Observational analysis shows improved estimation accuracy and robustness in high dimensions, suggesting the value of unlabeled samples.

Key Points

  • Additional unlabeled samples improve estimation accuracy in high-dimensional linear regression settings.
  • Robust semi-supervised estimators reduce estimation bias, even in model-favorable conditions.
  • Proposed semi-supervised methods enhance efficiency in sparse linear slope scenarios.
  • Extensive numerical studies confirm the effectiveness of these new estimation approaches.

Cite This Study

Chen et al. (2025) studied this question.

synapsesocial.com/papers/68c1d5e554b1d3bfb60f8a03https://doi.org/10.1093/biomtc/ujaf113
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