The Two-Stage approach to optimal \textit{non-linear} predictions via Generalized Ridge Regression is again illustrated. This time, we use a model with six $x-$predictors and more than $2,500$ observations. Unbiased estimates and predictions are then compared with their corresponding ``optimally biased'' estimates and predictions most likely to have minimal MSE risk under Normal distribution theory. Again, we find that lower residual standard errors and lower MSE risks relative to those lower errors result.

翻译：本文再次展示了通过广义岭回归实现最优非线性预测的两阶段方法。此次，我们采用一个包含六个自变量（$x-$预测变量）和超过2500个观测值的模型。将无偏估计与预测，与在正态分布理论下最可能具有最小均方误差风险的相应“最优有偏”估计与预测进行比较。我们再次发现，相比这些较低误差，残差标准误差和均方误差风险均有所降低。