Sensitivity analysis in multivariate methods: Decomposition of an arbitrary influence into a finite number of components
- 1 January 1990
- journal article
- research article
- Published by Taylor & Francis in Communications in Statistics - Theory and Methods
- Vol. 19 (4) , 1323-1341
- https://doi.org/10.1080/03610929008830264
Abstract
The influence function of the covariance matrix is decomposed into a finite number of components. This decomposition provides a useful tool to develop efficient methods for computing empirical influence curves related to various multivariate methods. It can also be used to characterize multivariate methods from the sensitivity perspective. A numerical example is given to demonstrate efficient computing and to characterize some procedures of exploratory factor analysis.Keywords
This publication has 11 references indexed in Scilit:
- Influential Observations in Principal Factor AnalysisPsychometrika, 1989
- Sensitivity analysis in maximum likelihood factor analysisCommunications in Statistics - Theory and Methods, 1989
- Influence functions related to eigenvalue problems which appear in multivariate analysisCommunications in Statistics - Theory and Methods, 1989
- Influential observations in principal component analysis:a case studyJournal of Applied Statistics, 1988
- Sensitivity analysis in principal component analysis:influence on the subspace spanned by principal components.Communications in Statistics - Theory and Methods, 1988
- Principal Component AnalysisPublished 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
- The Influence Curve and Its Role in Robust EstimationJournal of the American Statistical Association, 1974