Zhaoyuan Pan

Ridge Regression Outperforms Optimal Hard Sample-PC Selection in High-Dimensional Isotropic Gaussian Models

We study response-guided selection of sample principal components in isotropic Gaussian linear models with dimension proportional to sample size. Under an exact-cardinality constraint, we derive the risk-minimizing rule among coordinatewise binary selectors based on each sample e...

Ridge Regression Outperforms Optimal Hard Sample-PC Selection in High-Dimensional Isotropic Gaussian Models

We study response-guided selection of sample principal components in isotropic Gaussian linear models with dimension proportional to sample size. Under an exact-cardinality constraint, we derive the risk-minimizing rule among coordinatewise binary selectors based on each sample e...