Double Descent: Understanding Linear Model Estimation of Nonidentifiable Parameters and a Model for Overfitting
双重下降:理解不可识别参数的线性模型估计与过拟合模型
We consider ordinary least squares estimation and variations on least squares estimation such as penalized (regularized) least squares and spectral shrinkage estimates for problems with p > n and associated problems with prediction of new observations. After the introduction of Section 1, Section 2 examines a number of commonly used estimators for p > n. Section 3 introduces prediction with p > n. Section 4 introduces notational changes to facilitate discussion of…