Simple consistent cluster methods based on redescending M-estimators with an application to edge identification in images

DOI10.1016/j.jmva.2003.12.005zbMath1062.62114OpenAlexW2134761594MaRDI QIDQ1765621

Tim Garlipp, Christine H. Müller

Publication date: 23 February 2005

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2003.12.005

zbMATH Keywords

Kernel density estimationConsistencyM-estimationOrthogonal regressionEdge identification in noisy imagesMultivariate clusterRegression cluster

Mathematics Subject Classification ID

Density estimation (62G07) Nonparametric robustness (62G35) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Linear regression; mixed models (62J05) Image analysis in multivariate analysis (62H35)

Related Items (5)

Robust mixture regression using the \(t\)-distribution ⋮ Clusterwise functional linear regression models ⋮ Robust Linear Clustering ⋮ Robust fitting of mixture regression models ⋮ Detection of linear and circular shapes in image analysis

Cites Work

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• Robust regression: Asymptotics, conjectures and Monte Carlo
• Robust estimation in the errors-in-variables model
• Discrete Parameter Variation: Efficient Estimation of a Switching Regression Model
• Edge-Preserving Smoothers for Image Processing
• Robust Statistics

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