mlsanityData to 7 Oct 2026

Anomaly Detection by Robust Statistics

arXiv 1707.09752 ↗First posted 31 Jul 2017stat.MLcited by 22

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Abstract (arXiv)

Real data often contain anomalous cases, also known as outliers. These may spoil the resulting analysis but they may also contain valuable information. In either case, the ability to detect such anomalies is essential. A useful tool for this purpose is robust statistics, which aims to detect the outliers by first fitting the majority of the data and then flagging data points that deviate from it. We present an overview of several robust methods and the resulting graphical outlier detection tools. We discuss robust procedures for univariate, low-dimensional, and high-dimensional data, such as estimating location and scatter, linear regression, principal component analysis, classification, clustering, and functional data analysis. Also the challenging new topic of cellwise outliers is introduced.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Jul 2017.

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Citing papers per year: 2021: 2; 2022: 5; 2023: 6; 2024: 5; 2025: 3; 2026: 1.

22 papers.

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  1. 2023T-MARS: Improving Visual Representations by Circumventing Text Feature Learningcited by 43
  2. 2022Identifying a Training-Set Attack's Target Using Renormalized Influence Estimationcited by 32
  3. 2021A Pseudo-Metric between Probability Distributions based on Depth-Trimmed Regionscited by 17
  4. 2024Data Readiness for AI: A 360-Degree Surveycited by 15
  5. 2023Meta-survey on outlier and anomaly detectioncited by 12
  6. 2022Functional Anomaly Detection: a Benchmark Studycited by 10
  7. 2023Time-Uniform Confidence Spheres for Means of Random Vectorscited by 6
  8. 2024Self-Supervised Iterative Refinement for Anomaly Detection in Industrial Quality Controlcited by 5
  9. 2021Time Series Anomaly Detection with label-free Model Selectioncited by 3
  10. 2022Beyond Mahalanobis-Based Scores for Textual OOD Detectioncited by 3
  11. 2024Fairness at Every Intersection: Uncovering and Mitigating Intersectional Biases in Multimodal Clinical Predictionscited by 2
  12. 2023Fast kernel half-space depth for data with non-convex supportscited by 1
10 more
  1. 2024AR-Pro: Counterfactual Explanations for Anomaly Repair with Formal Propertiescited by 1
  2. 2025AutoGraphAD: Unsupervised network anomaly detection using Variational Graph Autoencoderscited by 1
  3. 2022Quantification of geogrid lateral restraint using transparent sand and deep learning-based image segmentationcited by 0
  4. 2022Challenges in anomaly and change point detectioncited by 0
  5. 2023Interruptions detection in video conferencescited by 0
  6. 2023Abnormal component analysiscited by 0
  7. 2024An Unsupervised Anomaly Detection in Electricity Consumption Using Reinforcement Learning and Time Series Forest Based Frameworkcited by 0
  8. 2025Deep learning and abstractive summarisation for radiological reports: an empirical study for adapting the PEGASUS models' family with scarce datacited by 0
  9. 2025Exploring LLM-based Frameworks for Fault Diagnosiscited by 0
  10. 2026Efficient Personalized Federated PCA with Manifold Optimization for IoT Anomaly Detectioncited by 0