Multicollinearity-agnostic feature screening for non-Euclidean responses: a factor adjusted approach
非欧几里得响应的抗多重共线性特征筛选:因子调整方法
In high-dimensional settings, multicollinearity is a pervasive issue that can substantially impair the performance of feature screening methods based on marginal Fréchet regression. Feature screening for non-Euclidean responses becomes unreliable when ultrahigh-dimensional predictors suffer from multicollinearity, because feature-specific signals may be masked by shared latent factors. To mitigate this effect, we propose a Factor adjusted Fréchet sure independence screening…