A note on sensitivity of principal component subspaces and the efficient detection of influential observations in high dimensions

DOI10.1214/08-EJS201zbMath1320.62140arXiv0803.0402OpenAlexW2000327239MaRDI QIDQ1951757

Luke A. Prendergast

Publication date: 24 May 2013

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0803.0402

zbMATH Keywords

perturbationprincipal component analysisinfluential observationsdistance between subspaces

Mathematics Subject Classification ID

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

Related Items

A new and practical influence measure for subsets of covariance matrix sample principal components with applications to high dimensional datasets ⋮ A concentration approach to sensitivity studies in statistical estimation problems ⋮ A correction of approximations used in sensitivity study of principal component analysis ⋮ Sensitivity of principal component subspaces: a comment on Prendergast's paper ⋮ Influence Functions for Dimension Reduction Methods: An Example Influence Study of Principal Hessian Direction Analysis ⋮ Comparative Study of Robust Estimators Based on a Sensitivity Coefficient in Principal Component Analysis

Uses Software

• LAPACK

Cites Work

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