Forward Regression via Gram-Schmidt Orthogonalization for Ultra-High Dimensional Linear Models
超高维线性模型中基于 Gram–Schmidt 正交化的前向回归
Forward regression is a classical and effective tool for variable screening in ultra-high dimensional linear models, but its standard projection-based implementation can be computationally costly and numerically unstable when predictors are strongly collinear. Motivated by this limitation, we propose an orthogonalized forward regression procedure, implemented recursively through Gram-Schmidt updates, that ranks predictors according to their unique contributions after…