This paper presents an integrated perspective on robustness in regression. Specifically, we examine the relationship between traditional outlier-resistant robust estimation and robust optimization, which focuses on parameter estimation resistant to imaginary dataset-perturbations. While both are commonly regarded as robust methods, these concepts demonstrate a bias-variance trade-off, indicating that they follow roughly converse strategies.

翻译：本文提出了关于回归分析中鲁棒性的综合视角。具体而言，我们研究了传统抗异常值鲁棒估计与鲁棒优化之间的关系，后者关注的是对虚构数据集扰动具有抵抗力的参数估计方法。尽管这两种方法通常都被视为鲁棒技术，但它们实际上呈现出偏差-方差权衡的特性，这表明二者遵循着大致相反的策略路径。