TY - JOUR
T1 - Influence functions and local influence in linear discriminant analysis
AU - Huang, Yufen
AU - Kao, Tzu Ling
AU - Wang, Tai Ho
N1 - Funding Information:
The authors would like to express their gratitude to Dr. Norman R. Draper, the Associated Editor and referees for many thoughtful comments and valuable suggestions. The first author is partially supported by a grant from the National Science Council of Taiwan (NSC-94-2118-M-194-004).
Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2007/5/1
Y1 - 2007/5/1
N2 - The perturbation theory provides a useful tool for the sensitivity analysis in linear discriminant analysis (LDA). Though some influence functions by single perturbation and local influence in LDA have been discussed in literature, we propose yet another influence function inspired by Critchley [1985. Influence in principal component analysis. Biometrika 72, 627-636], called the deleted empirical influence function, as an alternative approach for the influence analysis in LDA. It is well-known that single-perturbation diagnostics can suffer from the masking effect. Hence in this paper we also develop the pair-perturbation influence functions to detect the masked influential points. The comparisons between pair-perturbation influence functions and local influences in pairs in LDA are also investigated. Finally, two examples are provided to illustrate the results of these approaches.
AB - The perturbation theory provides a useful tool for the sensitivity analysis in linear discriminant analysis (LDA). Though some influence functions by single perturbation and local influence in LDA have been discussed in literature, we propose yet another influence function inspired by Critchley [1985. Influence in principal component analysis. Biometrika 72, 627-636], called the deleted empirical influence function, as an alternative approach for the influence analysis in LDA. It is well-known that single-perturbation diagnostics can suffer from the masking effect. Hence in this paper we also develop the pair-perturbation influence functions to detect the masked influential points. The comparisons between pair-perturbation influence functions and local influences in pairs in LDA are also investigated. Finally, two examples are provided to illustrate the results of these approaches.
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U2 - 10.1016/j.csda.2006.03.001
DO - 10.1016/j.csda.2006.03.001
M3 - Article
AN - SCOPUS:34047125237
SN - 0167-9473
VL - 51
SP - 3844
EP - 3861
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
IS - 8
ER -