Sensitivity analysis in principal component analysis:influence on the subspace spanned by principal components.
- 1 January 1988
- journal article
- research article
- Published by Taylor & Francis in Communications in Statistics - Theory and Methods
- Vol. 17 (9), 3157-3175
- https://doi.org/10.1080/03610928808829796
Abstract
The problem of detecting influential observations in principalcomponent analysis was discussed by several authors. Radhakrishnan and kshirsagar ( 1981 ), Critchley ( 1985 ), jolliffe ( 1986 )among others discussed this topicby using the influence functions I(X;θs)and I(X;Vs)of eigenvalues and eigenvectors, which wwere derived under the assumption that the eigenvalues of interest were simple. In this paper we propose the influence functionsI(X;∑q s=1θsVsVs T)and I(x;∑q s=1VsVs t)(q<p;p:number of variables) to investigate the influence onthe subspace spanned by principal components. These influence functions are applicable not only to the case where the edigenvalues of interst are all simple but also to the case where there are some multiple eigenvalues among those of interest.Keywords
This publication has 7 references indexed in Scilit:
- Principal Component AnalysisPublished by Springer Nature ,1986
- Statistical Software SAM — Sensitivity Analysis in Multivariate MethodsPublished by Springer Nature ,1986
- Influence in principal components analysisBiometrika, 1985
- Sensitivity Analysis in Hayashi’s Third Method of QuantificationBehaviormetrika, 1984
- Influence functions for certain parameters in multivariate analysisCommunications in Statistics - Theory and Methods, 1981
- Robust estimation and outlier detection with correlation coefficientsBiometrika, 1975
- The Influence Curve and its Role in Robust EstimationJournal of the American Statistical Association, 1974