PLS-Logistic Regression on Functional Data

Regression modeling in many fields, such as credit rating, banking industry and macroeconomic studies, is an important approach. However, Multicollinearity in the independent variable sets is harmful to Ordinary Least Squares (OLS) Regression. Partial Least Squares (PLS) Regression enables modeling under the condition of multicollinearity. In the fields of Credit Rating, many independent variables are related functional data, and the dependent variable is a categorical variable. For these problems, Functional PLS-Logistic Regression provides an approach of building regression model under the condition of multicollinearity. Empirical study shows that the GDP per capita of provinces in China has an obvious distribution feature which ensures the reasonability of classify the provinces according to their geography locations.