Additive Splines for Partial Least Squares Regression
本文提出一种将样条函数变换与偏最小二乘回归结合的加性样条偏最小二乘方法，用于处理非线性多响应回归问题，并通过化学和生理学应用展示了其性能。
Abstract This article introduces a generalization of the partial least squares regression (PLS). Transforming the predictors by means of spline functions is a useful way to extend PLS into nonlinearity and to obtain a multiresponse additive model. We describe both statistical and computational aspects of this new method, termed additive splines partial least squares (ASPLS). The performance of ASPLS compared with other PLS methods is illustrated with chemical and physiological applications. Key Words: Data reductionMultiresponse additive modelsPartial least squaresRegression splines.