We propose a new low-dimensional registration procedure that exploits the relation- ship between the response and the predictor in a function-on-function regression. In this context, functional covariance components (FCCs) provide a flexible and powerful tool to represent the data in a low-dimensional space, capturing the most meaningful modes of dependency between the two set of curves. Based on this reduced representation, our procedure aligns simultaneously the two sets of curves, in a way that optimizes the subsequent regression analysis. To implement our procedure, we use both the continuous registration (CR) algorithm and a novel parallel algorithm coded in R. We then compare it to other common registration approaches via simulations and an application to the AneuRisk data.

Covariance‐based low‐dimensional registration for function‐on‐function regression

Secchi, Piercesare;
2021-01-01

Abstract

We propose a new low-dimensional registration procedure that exploits the relation- ship between the response and the predictor in a function-on-function regression. In this context, functional covariance components (FCCs) provide a flexible and powerful tool to represent the data in a low-dimensional space, capturing the most meaningful modes of dependency between the two set of curves. Based on this reduced representation, our procedure aligns simultaneously the two sets of curves, in a way that optimizes the subsequent regression analysis. To implement our procedure, we use both the continuous registration (CR) algorithm and a novel parallel algorithm coded in R. We then compare it to other common registration approaches via simulations and an application to the AneuRisk data.
2021
functional data, regression, smoothing
File in questo prodotto:
Non ci sono file associati a questo prodotto.

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/11311/1181951
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact