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November 2015 Functional Data Analysis of Amplitude and Phase Variation
J. S. Marron, James O. Ramsay, Laura M. Sangalli, Anuj Srivastava
Statist. Sci. 30(4): 468-484 (November 2015). DOI: 10.1214/15-STS524


The abundance of functional observations in scientific endeavors has led to a significant development in tools for functional data analysis (FDA). This kind of data comes with several challenges: infinite-dimensionality of function spaces, observation noise, and so on. However, there is another interesting phenomena that creates problems in FDA. The functional data often comes with lateral displacements/deformations in curves, a phenomenon which is different from the height or amplitude variability and is termed phase variation. The presence of phase variability artificially often inflates data variance, blurs underlying data structures, and distorts principal components. While the separation and/or removal of phase from amplitude data is desirable, this is a difficult problem. In particular, a commonly used alignment procedure, based on minimizing the $\mathbb{L}^{2}$ norm between functions, does not provide satisfactory results. In this paper we motivate the importance of dealing with the phase variability and summarize several current ideas for separating phase and amplitude components. These approaches differ in the following: (1) the definition and mathematical representation of phase variability, (2) the objective functions that are used in functional data alignment, and (3) the algorithmic tools for solving estimation/optimization problems. We use simple examples to illustrate various approaches and to provide useful contrast between them.


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J. S. Marron. James O. Ramsay. Laura M. Sangalli. Anuj Srivastava. "Functional Data Analysis of Amplitude and Phase Variation." Statist. Sci. 30 (4) 468 - 484, November 2015.


Published: November 2015
First available in Project Euclid: 9 December 2015

zbMATH: 06946198
MathSciNet: MR3432837
Digital Object Identifier: 10.1214/15-STS524

Rights: Copyright © 2015 Institute of Mathematical Statistics


Vol.30 • No. 4 • November 2015
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