A Two-Stage approach is described that literally "straighten outs" any potentially nonlinear relationship between a y-outcome variable and each of p = 2 or more potential x-predictor variables. The y-outcome is then predicted from all p of these "linearized" spline-predictors using the form of Generalized Ridge Regression that is most likely to yield minimal MSE risk under Normal distribution-theory. These estimates are then compared and contrasted with those from the Generalized Additive Model that uses the same x-variables.

翻译：本文描述了一种两阶段方法，该方法能够真正“拉直”y-结果变量与p=2个或更多潜在x-预测变量之间任何潜在的非线性关系。然后，使用最有可能在正态分布理论下产生最小均方误差风险的广义岭回归形式，从所有这些“线性化”的样条预测变量对y-结果进行预测。最后，将这些估计值与使用相同x变量的广义可加模型得出的估计值进行比较和对比。