The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Computing depth contours of bivariate point clouds
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
Robust Regression and Outlier Detection
Rousseeuw, P. J., & Leroy, A. M. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/0471725382 · Cited by 5,609
Self-citation
Cited by
LOF: identifying density-based local outliers
Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335388 · Cited by 5,207
LOF: identifying density-based local outliers
Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) · Proceedings of the 2000 ACM SIGMOD international conference on Management of data · doi:10.1145/342009.335388 · Cited by 4,149
Graph based anomaly detection and description: a survey
Akoglu, L., Tong, H., & Koutra, D. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0365-y · Cited by 1,504
Distance-based outliers: algorithms and applications
Knorr, E. M., Ng, R. T., & Tucakov, V. (2000) · The VLDB Journal The International Journal on Very Large Data Bases · doi:10.1007/s007780050006 · Cited by 1,195
Angle-based outlier detection in high-dimensional data
Kriegel, H.-P., Schubert, M., & Zimek, A. (2008) · Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1401890.1401946 · Cited by 828
Metadata
- Authors
- Ruts, I., & Rousseeuw, P. J.
- Venue
- Computational Statistics & Data Analysis
- Publisher
- Elsevier BV
- Published
- 1996-11
- Volume
- 23
- Issue
- 1
- Pages
- 153-168
- ISSN
- 0167-9473 (ISSN-L)
- OpenAlex
- W2035006576
- Cited by
- 263
- References
- 1
- FWCI
- 7.92
- 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 depth contours of bivariate point clouds. https://app.cochat.ai/papers/10.1016/s0167-9473(96)00027-8
- The paper
- Ruts, I., & Rousseeuw, P. J. (1996). Computing depth contours of bivariate point clouds. Computational Statistics & Data Analysis. https://doi.org/10.1016/s0167-9473(96)00027-8
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.
