Comparative Study of Robust Estimators Based on a Sensitivity Coefficient in Principal Component Analysis

DOI10.1080/03610918.2012.762390zbMath1462.62370OpenAlexW2080429148MaRDI QIDQ2876173

Malika Cheikh

Publication date: 18 August 2014

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2012.762390

zbMATH Keywords

mean squared errorprincipal component analysisinfluence functionsensitivity coefficientrobust estimator

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Numerical computation of matrix norms, conditioning, scaling (65F35)

Cites Work

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• A new and practical influence measure for subsets of covariance matrix sample principal components with applications to high dimensional datasets
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• A note on sensitivity of principal component subspaces and the efficient detection of influential observations in high dimensions
• Influence function and efficiency of the minimum covariance determinant scatter matrix estimator
• Sensitivity analysis in principal component analysis:influence on the subspace spanned by principal components.
• Sensitivity Coefficient in Principal Component Analysis: Robust Case
• Influence in principal components analysis
• Highly robust estimation of dispersion matrices
• A class of robust principal component vectors.

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