Joint Continuum Regression for Multiple Predictands
本文推广了连续回归方法，通过联合构建多个响应变量的回归因子，试图改进单独预测每个响应变量的效果，并在真实和模拟数据上测试了新方法。
Abstract This article generalizes continuum regression (CR) in the hope that regressors “jointly constructed” for several predictands might improve on the separate prediction of individual predictands. The generalization developed is a mixture of principal components regression and de Jong's modification of partial least squares for multiple predictands. The balance of ingredients can be chosen by cross-validation, as can the number of regressors constructed. The new method has been tested on real and simulated data. The indications are that conditions for the superiority of the joint approach may be rare in practice.