An Approach for PLS Regression Modeling of Functional Data

摘要：

Partial Least Squares (PLS) approach is employed for linear regression modeling when both the dependent variables and independent variables are functional data (curves). After the introduction of the constant-style mean, variance and the correlative coefficient of functional data, an approach for PLs regression modeling of functional data is proposed to overcome the multicollinearity existing in the independent variables set An empirical study of the functional regression modeling shows that the proposed approach provides a tool for building regression model on functional data under the condition of multicollinearity. The empirical study conclusion, which is coincident with the wildly accepted economic theory, indicates that the Compensation of Employees is the most important variable that contributes to the Total Retail Sales of Consumer Goods in China, while the Government Revenue and Income of Enterprises are less important.

关键词： PLS regression Functional data Multicollinearity

作者: Shengshuai Wang JieWang HuiwenWang Gilbert Saporta

作者单位: Beijing University of Aeronautics and Astronautics, Beijing 100191, China Dagong Global Credit Rating Co., Ltd., Beijing, China Cedric.Conservatoire National des Arts et Metiers, Paris, France

会议类型: 国际会议

会议名称: The 6th International Conference on Partial Least Squares and Related Methods(第六届偏最小二乘及相关方法国际会议)

会议地点: 北京

会议语种:英文

页码: 28-33

在线出版日期: 2009-09-04（万方平台首次上网日期，不代表论文的发表时间）