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

dc.creatorPrendergast, Luke A.
dc.date2008-03-04
dc.date2008-06-26
dc.date.accessioned2026-07-07T12:17:25Z
dc.date.available2026-07-07T12:17:25Z
dc.descriptionIn this paper we introduce an influence measure based on second order expansion of the RV and GCD measures for the comparison between unperturbed and perturbed eigenvectors of a symmetric matrix estimator. Example estimators are considered to highlight how this measure compliments recent influence analysis. Importantly, we also show how a sample based version of this measure can be used to accurately and efficiently detect influential observations in practice.
dc.descriptionPublished in at http://dx.doi.org/10.1214/08-EJS201 the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/0803.0402
dc.identifierhttp://arxiv.org/abs/0803.0402
dc.identifierElectronic Journal of Statistics 2008, Vol. 2, 454-467
dc.identifierdoi:10.1214/08-EJS201
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/212073
dc.subjectStatistics Theory
dc.subject62F35 (Primary) 62H12 (Secondary)
dc.titleA note on sensitivity of principal component subspaces and the efficient detection of influential observations in high dimensions
dc.typetext

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