Nonlinear Structure Analysis with Partial Least-Squares RegressionBased on Spline Transformation
基于样条变换的PLS回归的非线性结构分析

Nonlinear Partial Least-Squares Regression Model based on Spline Transformation not only takes advantages of the characters of spline functions which can locally fit continuous curves properly, but also brings in Partial Least-Squares Regression Method which can effectively solve the problem of high correlations in the set of independent variables. In this paper, according to additive modeling methods both in theory and simulation, it is proven that Nonlinear Partial Least-Squares Regression Method based on Spline Transformation can not only get the exact whole forecasting model, but also successfully extract nonlinear features of each independent variable's effect on the dependent variable when dealing with nonlinear data systems with multi-absolute independent variables for one dependent variable. In this way, acquire the complex nonlinear structures of the data system and an explainable model can be acquired.