The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Computing and Graphing Highest Density Regions
Abstract
Many statistical methods involve summarizing a probability distribution by a region of the sample space covering a specified probability. One method of selecting such a region is to require it to contain points of relatively high density. Highest density regions are particularly useful for displaying multimodal distributions and, in such cases, may consist of several disjoint subsets—one for each local mode. In this paper, I propose a simple method for computing a highest density region from any given (possibly multivariate) density f(x) that is bounded and continuous in x. Several examples of the use of highest density regions in statistical graphics are also given. A new form of boxplot is proposed based on highest density regions; versions in one and two dimensions are given. Highest density regions in higher dimensions are also discussed and plotted.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Cox, D. R., & Hinkley, D. V. (1974) · doi:10.1007/978-1-4899-2887-0 · Cited by 3,089
Performance of Some Resistant Rules for Outlier Labeling
Hoaglin, D. C., Iglewicz, B., & Tukey, J. W. (1986) · Journal of the American Statistical Association · doi:10.1080/01621459.1986.10478363 · Cited by 773
Research works printed but not linked: 23
- 10.2307/2347385 · DOI · Paper page
- 10.1080/00031305.1987.10475467 · DOI
- 10.1080/00031305.1988.10475580 · DOI · Paper page
- Box , G. E. P. and Tiao , G. C. 1973 . Bayesian Inference in Statistical Analysis , Reading , MA : Addison-Wesley .
- Brillinger , D. R. , Guckenheimer , J. , Guttorp , P. E. and Oster , G. 1980 . “Empirical Modelling of Population Time Series Data: The Case of Age and Density Dependent Vital Rates” , Lectures on Mathematics in the Life Sciences Vol. 13 , 65 – 90 . Providence , RI : American Mathematical Society .
- Cook , R. D. and Weisberg , S. 1982 . Residuals and Influence in Regression , London : Chapman and Hall .
- 10.1080/00031305.1987.10475437 · doi:10.1080/00031305.1987.10475437
- Esty , W. W. and Banfield , J. D. 1992 . “The Box-Percentile Plot,” , Dept. of Mathematical Sciences Montana State University . Technical Report
- 10.1080/10618600.1994.10474629 · doi:10.1080/10618600.1994.10474629
- 10.1080/00401706.1992.10485280 · DOI
- Hyndman , R. J. 1990 . “An Algorithm for Constructing Highest Density Regions,” unpublished manuscript available from the author
- 10.1002/for.3980140503 · doi:10.1002/for.3980140503
- Hyndman , R. J. , Bashtannyk , D. M. and Grunwald , G. K. “Estimating and Visualizing Conditional Densities” . Journal of Computational Graphics and Statistics , 5 (in press)
- 10.1080/00031305.1978.10479236 · DOI · Paper page
- 10.1101/SQB.1957.022.01.017 · DOI · Paper page
- Scott , D. W. 1991 . “On Estimation and Visualization of Higher Dimensional Surfaces,” . In Computing and Graphics in Statistics , Edited by: Buja , A. and Tukey , P. A. New York : Springer-Verlag . (IMA Vol. 36 in Mathematics and Its Applications)
- 10.1002/9780470316849 · DOI · Paper page
- 10.1007/978-1-4684-0192-9 · DOI · Paper page
- Tong , H. 1988 . “Non-Linear Time Series Modelling in Population Biology: A Preliminary Case Study,” . In Nonlinear Time Series and Signal Processing , Lecture Notes in Control and Information Sciences Vol. 106 , 75 – 87 . New York : Springer-Verlag . · doi:10.1007/bfb0044276
- Tukey , J. W. 1977 . Exploratory Data Analysis , Reading , MA : Addison-Wesley .
- 10.1007/978-1-4899-4493-1 · DOI · Paper page
- Weisberg , S. 1985 . Applied Linear Regression , 2nd ed. , New York : John Wiley .
- 10.2307/2347864 · doi:10.2307/2347864
Cited by
Estimating Distance from Parallaxes. IV. Distances to 1.33 Billion Stars in Gaia Data Release 2
Bailer-Jones, C. A. L., Rybizki, J., Fouesneau, M., Mantelet, G., & Andrae, R. (2018) · The Astronomical Journal · doi:10.3847/1538-3881/aacb21 · Cited by 1,829
Pimentel, M. A. F., Clifton, D. A., Clifton, L., & Tarassenko, L. (2014) · Signal Processing · doi:10.1016/j.sigpro.2013.12.026 · Cited by 1,649
ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R
Duong, T. (2007) · Journal of Statistical Software · doi:10.18637/jss.v021.i07 · Cited by 593
The Bagplot: A Bivariate Boxplot
Rousseeuw, P. J., Ruts, I., & Tukey, J. W. (1999) · The American Statistician · doi:10.2307/2686061 · Cited by 568
Doing Bayesian Data Analysis: A Tutorial with R and BUGS
Barry, J. (2011) · Europe’s Journal of Psychology · doi:10.5964/ejop.v7i4.163 · Cited by 563
Metadata
- Authors
- Hyndman, R. J.
- Venue
- The American Statistician
- Publisher
- Informa UK Limited
- Published
- 1996-05
- Volume
- 50
- Issue
- 2
- Pages
- 120-126
- ISSN
- 0003-1305 (ISSN-L)
- 1537-2731
- OpenAlex
- W2005243481
- Cited by
- 720
- References
- 2
- FWCI
- 3.96
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Computing and Graphing Highest Density Regions. https://app.cochat.ai/papers/10.1080/00031305.1996.10474359
- The paper
- Hyndman, R. J. (1996). Computing and Graphing Highest Density Regions. The American Statistician. https://doi.org/10.1080/00031305.1996.10474359
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.