A Geometric Approach to Visualization of Variability in Functional Data

Weiyi Xie, Sebastian Kurtek, Karthik Bharath, Ying Sun

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

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.
Original languageEnglish (US)
Pages (from-to)979-993
Number of pages15
JournalJournal of the American Statistical Association
Volume112
Issue number519
DOIs
StatePublished - Dec 16 2016

Fingerprint

Dive into the research topics of 'A Geometric Approach to Visualization of Variability in Functional Data'. Together they form a unique fingerprint.

Cite this