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[WWW09] An Approach for PLS Regression Modeling of Functional Data
Conférence Internationale avec comité de lecture : PLS'09, 6th Int. Conf. on Partial Least Squares and Related Methods, Pékin, September 2009, pp.28-33,
motcle:
Résumé:
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.
Equipe:
msdma
BibTeX
| @inproceedings { | |||
| WWW09, | |||
| title | = | "{An Approach for PLS Regression Modeling of Functional Data}", | |
| author | = | " S. Wang and J. Wang and H. Wang and G. Saporta ", | |
| booktitle | = | "{PLS'09, 6th Int. Conf. on Partial Least Squares and Related Methods, Pékin}", | |
| year | = | 2009, | |
| month | = | "September", | |
| pages | = | "28-33", | |
| } | |||
