Type
ArticleAuthors
Dai, WenlinGenton, Marc G.

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionStatistics Program
Date
2018-08-24Preprint Posting Date
2017-03-19Online Publication Date
2018-08-24Print Publication Date
2018-10-02Permanent link to this record
http://hdl.handle.net/10754/626524
Metadata
Show full item recordAbstract
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.Citation
Dai W, Genton MG (2018) Multivariate Functional Data Visualization and Outlier Detection. Journal of Computational and Graphical Statistics: 1–12. Available: http://dx.doi.org/10.1080/10618600.2018.1473781.Sponsors
This research was supported by the King Abdullah University of Science and Technology (KAUST).Publisher
Informa UK LimitedarXiv
1703.06419Additional Links
https://www.tandfonline.com/doi/full/10.1080/10618600.2018.1473781Relations
Is Supplemented By:- [Dataset]
Wenlin Dai, & Genton, M. G. (2018). Multivariate Functional Data Visualization and Outlier Detection [Data set]. Taylor & Francis. https://doi.org/10.6084/M9.FIGSHARE.6308771.V1. DOI: 10.6084/m9.figshare.6308771.v1 Handle: 10754/664192
ae974a485f413a2113503eed53cd6c53
10.1080/10618600.2018.1473781