In this work we introduce a general framework for distribution-free predictive inference in Geographically Weighted Functional Regression (GWFR) using conformal inference. A prediction band for the functional response variable using a functional estimator of the regression function and a new non conformity mea- sure is thus introduced. We also investigate our procedure for producing prediction bands with locally varying length, in order to adapt the approach in presence of het- eroskedasticity in the data. An application for environmental impact assessment is proposed.

IES 2022 Innovation & Society 5.0: Statistical and Economic Methodologies for Quality Assessment

Andrea Diana
Membro del Collaboration Group
;
Elvira Romano
Membro del Collaboration Group
;
Antonio Irpino
Membro del Collaboration Group
2022

Abstract

In this work we introduce a general framework for distribution-free predictive inference in Geographically Weighted Functional Regression (GWFR) using conformal inference. A prediction band for the functional response variable using a functional estimator of the regression function and a new non conformity mea- sure is thus introduced. We also investigate our procedure for producing prediction bands with locally varying length, in order to adapt the approach in presence of het- eroskedasticity in the data. An application for environmental impact assessment is proposed.
978-88-94593-35-8
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11591/465595
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