A General Framework for Multivariate Functional Principal Component Analysis of Amplitude and Phase Variation

MCML Authors

Fabian Scheipl

PD Dr.

Principal Investigator

Functional Data Analysis

Abstract

Functional data typically contain amplitude and phase variation. In many data situations, phase variation is treated as a nuisance effect and is removed during preprocessing, although it may contain valuable information. In this note, we focus on joint principal component analysis (PCA) of amplitude and phase variation. As the space of warping functions has a complex geometric structure, one key element of the analysis is transforming the warping functions to urn:x-wiley:sta4:media:sta4220:sta4220-math-0001. We present different transformation approaches and show how they fit into a general class of transformations. This allows us to compare their strengths and limitations. In the context of PCA, our results offer arguments in favour of the centred log-ratio transformation. We further embed two existing approaches from the literature for joint PCA of amplitude and phase variation into the framework of multivariate functional PCA, where we study the properties of the estimators based on an appropriate metric. The approach is illustrated through an application from seismology.

article

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Stat

8.2. Feb. 2019.

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Authors

C. Happ • F. Scheipl • A.-A. Gabriel • S. Greven

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Links

DOI

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Research Area

 A1 | Statistical Foundations & Explainability