Outlier detection in the multiple cluster setting using the minimum covariance determinant estimator

DOI10.1016/S0167-9473(02)00280-3zbMath1430.62133OpenAlexW2069051541MaRDI QIDQ956805

Johanna Hardin, David M. Rocke

Publication date: 26 November 2008

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0167-9473(02)00280-3

zbMATH Keywords

outlier detectionminimum covariance determinantrobust clustering

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Robustness and adaptive procedures (parametric inference) (62F35)

Related Items

EXPECTED MAXIMIZATION ALGORITHM: PROJECTION DEPTH APPROACH ⋮ A robust EM clustering approach: ROBEM ⋮ Robust inference for parsimonious model-based clustering ⋮ Three-way decision-based co-detection for outliers ⋮ High-breakdown robust multivariate methods ⋮ A reweighting approach to robust clustering ⋮ Object selection in credit scoring using covariance matrix of parameters estimations ⋮ Robust Detection of Multiple Outliers in Grouped Multivariate Data ⋮ A review of robust clustering methods ⋮ A useful approach to identify the multicollinearity in the presence of outliers ⋮ Robust concentration graph model selection ⋮ A robust linear grouping algorithm ⋮ Scale-invariant clustering with minimum volume ellipsoids ⋮ Linear grouping using orthogonal regression ⋮ Exploring the number of groups in robust model-based clustering ⋮ The minimum regularized covariance determinant estimator ⋮ Robust fitting of mixtures using the trimmed likelihood estimator ⋮ Robust double clustering: a method based on alternating concentration steps ⋮ Robust Coordinate Descent Algorithm Robust Solution Path for High-dimensional Sparse Regression Modeling ⋮ Separating a mixture of two normals with proportional covariances ⋮ Monitoring Variation in a Multivariate Process When the Dimension is Large Relative to the Sample Size ⋮ The importance of the scales in heterogeneous robust clustering ⋮ Nonparametric and robust methods. (Editorial) ⋮ Kernel ellipsoidal trimming ⋮ Outlier identification in model-based cluster analysis

Uses Software

• mclust
• EMMIX
• CerioliOutlierDetection

Cites Work

• Breakdown points of affine equivariant estimators of multivariate location and covariance matrices
• Improved feasible solution algorithms for high breakdown estimation.
• MCLUST: Software for model-based cluster analysis
• Influence function and efficiency of the minimum covariance determinant scatter matrix estimator
• Least Median of Squares Regression
• Fast Very Robust Methods for the Detection of Multiple Outliers
• Identification of Outliers in Multivariate Data
• Appropriate Critical Values When Testing for a Single Multivariate Outlier by Using the Mahalanobis Distance
• The Behavior of the Stahel-Donoho Robust Multivariate Estimator
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