Robust Reduced Rank Regression for Heavy-Tailed Noise and Missing Data
Published:Dec 30, 2025 20:09
•1 min read
•ArXiv
Analysis
This paper addresses the limitations of classical Reduced Rank Regression (RRR) methods, which are sensitive to heavy-tailed errors, outliers, and missing data. It proposes a robust RRR framework using Huber loss and non-convex spectral regularization (MCP and SCAD) to improve accuracy in challenging data scenarios. The method's ability to handle missing data without imputation and its superior performance compared to existing methods make it a valuable contribution.
Key Takeaways
- •Proposes a robust RRR framework to handle heavy-tailed noise, outliers, and missing data.
- •Combines Huber loss with non-convex spectral regularization (MCP and SCAD).
- •Handles missing data without imputation.
- •Outperforms existing methods in simulations and real-world data.
- •Provides an R package (rrpackrobust) for implementation.
Reference
“The proposed methods substantially outperform nuclear-norm-based and non-robust alternatives under heavy-tailed noise and contamination.”