# Anomaly Detection by Robust Statistics

arXiv: https://arxiv.org/abs/1707.09752 · HTML: https://mlsanity.com/paper/1707.09752/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2017.

## Built on by (most cited first, 23 of 23)

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- [An Unsupervised Anomaly Detection in Electricity Consumption Using Reinforcement Learning and Time Series Forest Based Framework](https://mlsanity.com/paper/2501.00107/) (2024)
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- [Efficient Personalized Federated PCA with Manifold Optimization for IoT Anomaly Detection](https://mlsanity.com/paper/2602.12622/) (2026)
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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.