A geometric approach to visualization of variability in functional data

Xie, Weiyi, Kurtek, Sebastian, Bharath, Karthik and Sun, Ying (2016) A geometric approach to visualization of variability in functional data. Journal of the American Statistical Association, 112 (519). pp. 979-993. ISSN 1537-274X

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We propose a new method for the construction and visualization of boxplot-type displays for functional data. We use a recent functional data analysis framework, based on a representation of functions called square-root slope functions, to decompose observed variation in functional data into three main components: amplitude, phase, and vertical translation. We then construct separate displays for each component, using the geometry and metric of each representation space, based on a novel definition of the median, the two quartiles, and extreme observations. The outlyingness of functional data is a very complex concept. Thus, we propose to identify outliers based on any of the three main components after decomposition. We provide a variety of visualization tools for the proposed boxplot-type displays including surface plots. We evaluate the proposed method using extensive simulations and then focus our attention on three real data applications including exploratory data analysis of sea surface temperature functions, electrocardiogram functions and growth curves.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/833175
Additional Information: The Version of Record of this manuscript has been published and is available in Journal of the American Statistical Association 16 Dec 2016 http://www.tandfonline.com/doi/full/10.1080/01621459.2016.1256813
Keywords: Amplitude and phase variabilities, Fisher–Rao metric, Functional outlier detection, Square-root slope function
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Mathematical Sciences
Identification Number: https://doi.org/10.1080/01621459.2016.1256813
Depositing User: Bharath, Karthik
Date Deposited: 23 Feb 2017 15:42
Last Modified: 04 May 2020 18:25
URI: https://eprints.nottingham.ac.uk/id/eprint/40806

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