A distributed framework with trace-similarity penalty and invex relaxation achieves two-phase minimax optimal rates and sharper model-free prediction error bounds under unidentifiable parameters and heterogeneity.
Sparse least trimmed squares regression for analyzing high-dimensional large data sets
4 Pith papers cite this work. Polarity classification is still indexing.
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stat.ME 4years
2026 4verdicts
UNVERDICTED 4representative citing papers
Proposes a covariance-aware tuning-free shrinkage framework and sequential algorithm for multi-source estimation that attains oracle risk asymptotically and improves on single-step methods.
TransL2E performs robust transfer learning in structured regression by accounting for contamination via L2E and detecting useful sources at both individual and cohort levels.
Trans-RR improves asymptotic estimation error in moderate-dimensional ridge-regularized robust regression by transferring a source-study estimator and applying a target-study correction.
citing papers explorer
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Distributed Prediction under Heterogeneity with Unidentifiable Parameter
A distributed framework with trace-similarity penalty and invex relaxation achieves two-phase minimax optimal rates and sharper model-free prediction error bounds under unidentifiable parameters and heterogeneity.
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Tuning-Free Efficient Estimation for Multi-Source Data via Covariance-Aware Shrinkage
Proposes a covariance-aware tuning-free shrinkage framework and sequential algorithm for multi-source estimation that attains oracle risk asymptotically and improves on single-step methods.
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Transfer Learning for Robust Structured Regression with Bi-level Source Detection
TransL2E performs robust transfer learning in structured regression by accounting for contamination via L2E and detecting useful sources at both individual and cohort levels.
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Transfer Learning for Moderate-Dimensional Ridge-Regularized Robust Linear Regression
Trans-RR improves asymptotic estimation error in moderate-dimensional ridge-regularized robust regression by transferring a source-study estimator and applying a target-study correction.