Abstract
This article proposes a new graphical tool, the magnitude-shape (MS) plot, for visualizing both the magnitude and shape outlyingness of multivariate functional data. The proposed tool builds on the recent notion of functional directional outlyingness, which measures the centrality of functional data by simultaneously considering the level and the direction of their deviation from the central region. The MS-plot intuitively presents not only levels but also directions of magnitude outlyingness on the horizontal axis or plane, and demonstrates shape outlyingness on the vertical axis. A dividing curve or surface is provided to separate nonoutlying data from the outliers. Both the simulated data and the practical examples confirm that the MS-plot is superior to existing tools for visualizing centrality and detecting outliers for functional data. Supplementary material for this article is available online.
Original language | English (US) |
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Pages (from-to) | 923-934 |
Number of pages | 12 |
Journal | JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS |
Volume | 27 |
Issue number | 4 |
DOIs | |
State | Published - Oct 2 2018 |
Bibliographical note
Publisher Copyright:© 2018, © 2018 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.
Keywords
- Data visualization
- Directional outlyingness
- Functional data
- Graphical tool
- Magnitude and shape
- Outlier detection
ASJC Scopus subject areas
- Statistics and Probability
- Discrete Mathematics and Combinatorics
- Statistics, Probability and Uncertainty
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Multivariate Functional Data Visualization and Outlier Detection
Dai, W. (Creator) & Genton, M. G. (Creator), figshare, 2018
DOI: 10.6084/m9.figshare.6308771.v1, http://hdl.handle.net/10754/664192
Dataset