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

Tobia Boschi, Francesca Chiaromonte, Piercesare Secchi, Bing Li

Research output: Contribution to journalArticlepeer-review


We propose a new low-dimensional registration procedure that exploits the relationship 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.

Original languageEnglish (US)
Article numbere404
Issue number1
StatePublished - Dec 2021

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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