Robust Sliced Inverse Regression: Optimal Estimation for Heavy-Tailed Data in High Dimensions
稳健切片逆回归：高维重尾数据的最优估计
Sliced inverse regression (SIR) is a flexible modeling tool that effectively reduces dimensions to reveal the complicated mechanism behind data. In recent years, SIR has been generalized to high dimensions in a variety of ways. However, all existing methods rely on the light-tailed assumption for predictors, which is frequently violated in real life. To tackle ubiquitous heavy-tailed data, we propose a novel robust SIR method, referred to as ROSE, that scales well with high…