We present an approach for modelling multivariate dependent functional data. To account for the dominant structural features of the data, we rely on the theory of Gaussian Processes and extend hierarchical dynamic linear models for multivariate time series to the functional data setting. We illustrate the proposed methodology within the framework of bivariate functional data and discuss problems referring to detection of spatial patterns and curve prediction.

Coupled Gaussian Processes for Functional Data Analysis

Fontanella Lara;Fontanella Sara;Ippoliti Luigi;Valentini Pasquale
2019-01-01

Abstract

We present an approach for modelling multivariate dependent functional data. To account for the dominant structural features of the data, we rely on the theory of Gaussian Processes and extend hierarchical dynamic linear models for multivariate time series to the functional data setting. We illustrate the proposed methodology within the framework of bivariate functional data and discuss problems referring to detection of spatial patterns and curve prediction.
2019
9788891915108
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/11564/724652
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact