Functional regression is a statistical method that is used to model the relationship between a response variable and a set of predictor variables that are functions. Functional Partial Least-Squares (PLS) regression is a form of functional regression analysis that is particularly useful when the number of predictors is large compared to the number of observations, or when the predictors are highly correlated. The basic idea of functional PLS via regression splines is to transform both response and predictors by using a set of splinebasis functions, such as B-spline basis, and then use the standard PLS technique to estimate the optimal transformed predictors. We show its performance on a real data set concerning the sustainable development goals of Agenda 2030.
Functional Partial Least-Squares via Regression Splines. An application on Italian Sustainable Development Goals data
Ida Camminatiello
;Rosaria Lombardo;
2023
Abstract
Functional regression is a statistical method that is used to model the relationship between a response variable and a set of predictor variables that are functions. Functional Partial Least-Squares (PLS) regression is a form of functional regression analysis that is particularly useful when the number of predictors is large compared to the number of observations, or when the predictors are highly correlated. The basic idea of functional PLS via regression splines is to transform both response and predictors by using a set of splinebasis functions, such as B-spline basis, and then use the standard PLS technique to estimate the optimal transformed predictors. We show its performance on a real data set concerning the sustainable development goals of Agenda 2030.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
