A Simulation Study of Some Ridge Estimators
用蒙特卡洛方法系统评估了10种岭回归偏倚参数估计算法，发现3种整体表现较好，一种双参数估计量有潜力优于单参数岭估计量。
Abstract Ridge regression, which defines a class of estimators indexed by a biasing parameter k, is an alternative to least squares estimation in the multiple linear regression model. Many algorithms for the biasing parameter have been proposed in the statistical literature. The present study identifies 10 promising algorithms and systematically evaluates and compares them using Monte Carlo methods. Three algorithms perform well overall. A two-parameter estimator offers potential improvement over one-parameter ridge estimators.