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 / Alaimo, Leonardo; Camminatiello, Ida; Lombardo, Rosaria; Durand, Jean-Francois. - (2023), pp. 232-237. (Intervento presentato al convegno Sis 2023 - Statistical Learning; Sustainability and Impact Evaluation tenutosi a Ancona).

Functional Partial Least-Squares via Regression Splines. An application on Italian Sustainable Development Goals data

Leonardo Alaimo;
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.
2023
Sis 2023 - Statistical Learning; Sustainability and Impact Evaluation
B-splines; Nodal coefficients; Partial Least-Squares; Sustainability
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Functional Partial Least-Squares via Regression Splines. An application on Italian Sustainable Development Goals data / Alaimo, Leonardo; Camminatiello, Ida; Lombardo, Rosaria; Durand, Jean-Francois. - (2023), pp. 232-237. (Intervento presentato al convegno Sis 2023 - Statistical Learning; Sustainability and Impact Evaluation tenutosi a Ancona).
File allegati a questo prodotto
File Dimensione Formato  
Camminatiello_functional-partial_2023.pdf

accesso aperto

Tipologia: Licenza (contratto editoriale)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 109.65 MB
Formato Adobe PDF
109.65 MB Adobe PDF

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1713071
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact