Methods of \(L_ 1\) estimation of a covariance matrix

DOI10.1016/0167-9473(87)90054-5zbMath0623.62043OpenAlexW1608792154MaRDI QIDQ1091707

Douglas M. Hawkins, Jacqueline S. Galpin

Publication date: 1987

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

Full work available at URL: https://doi.org/10.1016/0167-9473(87)90054-5

zbMATH Keywords

linear and quadratic programming\(L_ 1\)-estimationprincipal component estimationrobust covariance matrix estimatesspectral decomposition of a covariance matrix

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Estimation in multivariate analysis (62H12) Robustness and adaptive procedures (parametric inference) (62F35)

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On principal component analysis in \(L_{1}\). ⋮ A pure \(L_1\)-norm principal component analysis ⋮ Deep survival algorithm based on nuclear norm ⋮ Robust multi-view discriminant analysis with view-consistency ⋮ Sign eigenanalysis and its applications to optimization problems and robust statistics ⋮ L1-norm projection pursuit principal component analysis ⋮ The optimality of the centroid method ⋮ Transposition invariant principal component analysis in \(L_{1}\) for long tailed data ⋮ Robust Q-mode principal component analysis in \(L_{1}\) ⋮ Robust centroid method

Cites Work

• Robust m-estimators of multivariate location and scatter
• The regression dilemma
• Projection-Pursuit Approach to Robust Dispersion Matrices and Principal Components: Primary Theory and Monte Carlo
• Robust Estimation of Dispersion Matrices and Principal Components
• Robust Procedures in Multivariate Analysis I: Robust Covariance Estimation
• Robust Statistics
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