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，该方法能真正“拉直”y-结果变量与p≥2个潜在x-预测变量之间任何潜在的非线性关系。随后，利用正态分布理论下最可能实现均方误差风险最小化的广义岭回归形式，基于所有这些“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所得的估计值进行比较和对比。