# Robust statistics for outlier detection

@article{Rousseeuw2011RobustSF, title={Robust statistics for outlier detection}, author={Peter J. Rousseeuw and Mia Hubert}, journal={Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery}, year={2011}, volume={1} }

When analyzing data, outlying observations cause problems because they may strongly influence the result. [] Key Method We discuss robust procedures for univariate, low-dimensional, and high-dimensional data such as estimation of location and scatter, linear regression, principal component analysis, and classification. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 73-79 DOI: 10.1002/widm.2
This article is categorized under:
Algorithmic Development > Biological Data Mining…

## 467 Citations

### Outliers and Robustness for Ordinal Data

- Mathematics
- 2011

This chapter tackles the topics of robustness and multivariate outlier detection for ordinal data. We initially review outlier detection methods in regression for continuous data and give an example…

### There and back again: Outlier detection between statistical reasoning and data mining algorithms

- Computer ScienceWIREs Data Mining Knowl. Discov.
- 2018

From a joint point of view of data mining and statistics the roots and the path of development of statistical outlier Detection and of database‐related data mining methods for outlier detection are detailed.

### Outlier Detection in Logistic Regression: A Quest for Reliable Knowledge from Predictive Modeling and Classification

- Computer Science2012 IEEE 12th International Conference on Data Mining Workshops
- 2012

A group deletion approach based diagnostic measure for identifying multiple influential observations in logistic regression and a plotting technique that can classify data into outliers, high leverage points, as well as influential and regular observations are introduced.

### Multivariate voronoi outlier detection for time series

- Computer Science2014 IEEE Healthcare Innovation Conference (HIC)
- 2014

The approach copes with outliers in a multivariate framework, via designing and extracting effective attributes or features from the data that can take parametric or nonparametric forms.

### Evaluation of outlier detection method performance in symmetric multivariate distributions

- MathematicsCommun. Stat. Simul. Comput.
- 2020

Evaluating the blocked adaptive computationally efficient outlier nominators (BACON), the fast minimum covariance determinant (FAST-MCD), and the robust Mahalanobis distance (RM) method in multivariate data sets indicates that the performance of these methods varies according to the distribution type.

### An Overview of Multiple Outliers in Multidimensional Data

- Computer Science
- 2013

An overview of multivariate outlier detection methods is provided because of its growing importance in a wide variety of practical situations and because the difficulty of detection increases with the number of outliers and the dimension of the data.

### Data perturbation for outlier detection ensembles

- Computer ScienceSSDBM '14
- 2014

Data perturbation is proposed as a new technique to induce diversity inindividual outlier detectors as well as a rank accumulation method for the combination of the individual outlier rankings in order to construct an outlier detection ensemble.

### Outlier Detection Based on Low Density Models

- Computer Science2018 IEEE International Conference on Data Mining Workshops (ICDMW)
- 2018

Results show that SDO satisfactorily competes with the best ranked outlier detection alternatives and makes SDO highly flexible and adaptable to stand-alone frameworks that must detect outliers fast with accuracy rates equivalent to lazy learning algorithms.

### Regression and Outliers

- Computer Science, Economics
- 2019

It is pointed out that robust methods can and should be used for outlier detection, because outliers often contain additional information and are thus important and thus important.

### Improved outlier detection using sparse coding-based methods

- Computer SciencePattern Recognit. Lett.
- 2019

## References

SHOWING 1-10 OF 63 REFERENCES

### High-Breakdown Robust Multivariate Methods

- Mathematics
- 2008

When applying a statistical method in practice it often occurs that some observations deviate from the usual assumptions. However, many classical methods are sensitive to outliers. The goal of robust…

### Unmasking Multivariate Outliers and Leverage Points

- Computer Science, Mathematics
- 1990

This work proposes to compute distances based on very robust estimates of location and covariance, better suited to expose the outliers in a multivariate point cloud, to avoid the masking effect.

### Robust Regression and Outlier Detection

- GeologyWiley Series in Probability and Statistics
- 1987

This paper presents the results of a two-year study of the statistical treatment of outliers in the context of one-Dimensional Location and its applications to discrete-time reinforcement learning.

### Robust Estimates of Location and Dispersion for High-Dimensional Datasets

- Computer ScienceTechnometrics
- 2002

An estimator of location and scatter based on a modified version of the Gnanadesikan–Kettenring robust covariance estimate is proposed, which is as good as or better than SD and FMCD at detecting outliers and other structures, with much shorter computing times.

### A robust method for cluster analysis

- Mathematics
- 2005

Let there be given a contaminated list of n R d -valued observations coming from g different, normally distributed populations with a common covariance matrix. We compute the ML-estimator with…

### Fast and robust discriminant analysis

- Mathematics, Computer ScienceComput. Stat. Data Anal.
- 2004

### Computing LTS Regression for Large Data Sets

- Computer Science, MathematicsData Mining and Knowledge Discovery
- 2005

For small data sets FAST-LTS typically finds the exact LTS, whereas for larger data sets it gives more accurate results than existing algorithms for LTS and is faster by orders of magnitude.

### Robust linear discriminant analysis using S‐estimators

- Mathematics
- 2001

The authors consider a robust linear discriminant function based on high breakdown location and covariance matrix estimators. They derive influence functions for the estimators of the parameters of…

### ROBPCA: A New Approach to Robust Principal Component Analysis

- Computer ScienceTechnometrics
- 2005

The ROBPCA approach, which combines projection pursuit ideas with robust scatter matrix estimation, yields more accurate estimates at noncontaminated datasets and more robust estimates at contaminated data.