The DOI matches its Crossref record; 20 of the 21 references it cites were found in a registry or an index.
The Bagplot: A Bivariate Boxplot
Abstract
We propose the bagplot, a bivariate generalization of the univariate boxplot. The key notion is the half space location depth of a point relative to a bivariate dataset, which extends the univariate concept of rank. The “depth median” is the deepest location, and it is surrounded by a “bag” containing the n/2 observations with largest depth. Magnifying the bag by a factor 3 yields the “fence” (which is not plotted). Observations between the bag and the fence are marked by a light gray loop, whereas observations outside the fence are flagged as outliers. The bagplot visualizes the location, spread, correlation, skewness, and tails of the data. It is equivariant for linear transformations, and not limited to elliptical distributions. Software for drawing the bagplot is made available for the S-Plus and MATLAB environments. The bagplot is illustrated on several datasets—for example, in a scatterplot matrix of multivariate data.
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“These statistics are often depicted graphically as error bars and boxplots for univariate data, and two dimensional error bars and bagplots for bivariate data [109].”
From Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty (2021) · cites this paper as [109]
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Cites
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Mathematics and the Picturing of Data
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Self-citationComputing and Graphing Highest Density Regions
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Breakdown Properties of Location Estimates Based on Halfspace Depth and Projected Outlyingness
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(1994) · Springer eBooks · doi:10.1007/978-1-4899-7266-8 · Cited by 365
Computing depth contours of bivariate point clouds
Ruts, I., & Rousseeuw, P. J. (1996) · Computational Statistics & Data Analysis · doi:10.1016/s0167-9473(96)00027-8 · Cited by 263
Self-citation
Research works printed but not linked: 6
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Cited by
An adjusted boxplot for skewed distributions
Hubert, M., & Vandervieren, E. (2008) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2007.11.008 · Cited by 834
The diet‐body offset in human nitrogen isotopic values: A controlled dietary study
O'Connell, T. C., Kneale, C. J., Tasevska, N., & Kuhnle, G. G. C. (2012) · American Journal of Physical Anthropology · doi:10.1002/ajpa.22140 · Cited by 461
Sun, Y., & Genton, M. G. (2011) · Journal of Computational and Graphical Statistics · doi:10.1198/jcgs.2011.09224 · Cited by 459
Hierarchical Aggregation for Information Visualization: Overview, Techniques, and Design Guidelines
Elmqvist, N., & Fekete, J.-D. (2010) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2009.84 · Cited by 381
Brain Morphological Signatures for Chronic Pain
Baliki, M. N., Schnitzer, T. J., Bauer, W. R., & Apkarian, A. V. (2011) · PLoS ONE · doi:10.1371/journal.pone.0026010 · Cited by 371
Metadata
- Authors
- Rousseeuw, P. J., Ruts, I., & Tukey, J. W.
- Affiliations
- University of Antwerp (BE)
- Princeton University (US)
- ORCID
- Rousseeuw, Peter J.
- Venue
- The American Statistician
- Publisher
- Informa UK Limited
- Published
- 1999-11
- Volume
- 53
- Issue
- 4
- Pages
- 382-387
- ISSN
- 0003-1305 (ISSN-L)
- 1537-2731
- OpenAlex
- W2043872985
- Cited by
- 568
- References
- 21
- Citations by year
- 2026: 14 · 2025: 29 · 2024: 22 · 2023: 23 · 2022: 37 · 2021: 38 · 2020: 33 · 2019: 30 · 2018: 30 · 2017: 35 · 2016: 28 · 2015: 38 · 2014: 33 · 2013: 20 · 2012: 22
- FWCI
- 6.83
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The American Statistician (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Data Visualization and Analytics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Keywords
- outlier detection, scatterplot matrix, multivariate data visualization
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
