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

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.
9788891915108
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11564/724652
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