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

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

2008-03-04

arxiv.org/abs/0803.0402v2

Electronic Journal of Statistics 2008, Vol. 2, 454-467

Mathematics

Statistics Theory

Published in at http://dx.doi.org/10.1214/08-EJS201 the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by t

Scientific paper

10.1214/08-EJS201

In 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.

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