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Inference for robust canonical variate analysis

Stefan Van Aelst and Gert Willems UGent (2010) ADVANCES IN DATA ANALYSIS AND CLASSIFICATION. 4(2-3). p.181-197
abstract
We consider the problem of optimally separating two multivariate populations. Robust linear discriminant rules can be obtained by replacing the empirical means and covariance in the classical discriminant rules by S or MM-estimates of location and scatter. We propose to use a fast and robust bootstrap method to obtain inference for such a robust discriminant analysis. This is useful since classical bootstrap methods may be unstable as well as extremely time-consuming when robust estimates such as S or MM-estimates are involved. In particular, fast and robust bootstrap can be used to investigate which variables contribute significantly to the canonical variate, and thus the discrimination of the classes. Through bootstrap, we can also examine the stability of the canonical variate. We illustrate the method on some real data examples.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
BOOTSTRAP, REGRESSION, S-ESTIMATORS, LINEAR DISCRIMINANT-ANALYSIS, MULTIVARIATE LOCATION, MATRICES, COVARIANCE, MODEL, Bootstrap, Canonical variate, Linear discriminant analysis, Robustness
journal title
ADVANCES IN DATA ANALYSIS AND CLASSIFICATION
Adv. Data Anal. Classif.
volume
4
issue
2-3
pages
181 - 197
Web of Science type
Article
Web of Science id
000284145800007
JCR category
STATISTICS & PROBABILITY
JCR impact factor
0.581 (2010)
JCR rank
86/110 (2010)
JCR quartile
4 (2010)
ISSN
1862-5347
DOI
10.1007/s11634-010-0063-6
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
1241653
handle
http://hdl.handle.net/1854/LU-1241653
date created
2011-05-25 14:16:31
date last changed
2016-12-19 15:44:47
@article{1241653,
  abstract     = {We consider the problem of optimally separating two multivariate populations. Robust linear discriminant rules can be obtained by replacing the empirical means and covariance in the classical discriminant rules by S or MM-estimates of location and scatter. We propose to use a fast and robust bootstrap method to obtain inference for such a robust discriminant analysis. This is useful since classical bootstrap methods may be unstable as well as extremely time-consuming when robust estimates such as S or MM-estimates are involved. In particular, fast and robust bootstrap can be used to investigate which variables contribute significantly to the canonical variate, and thus the discrimination of the classes. Through bootstrap, we can also examine the stability of the canonical variate. We illustrate the method on some real data examples.},
  author       = {Van Aelst, Stefan and Willems, Gert},
  issn         = {1862-5347},
  journal      = {ADVANCES IN DATA ANALYSIS AND CLASSIFICATION},
  keyword      = {BOOTSTRAP,REGRESSION,S-ESTIMATORS,LINEAR DISCRIMINANT-ANALYSIS,MULTIVARIATE LOCATION,MATRICES,COVARIANCE,MODEL,Bootstrap,Canonical variate,Linear discriminant analysis,Robustness},
  language     = {eng},
  number       = {2-3},
  pages        = {181--197},
  title        = {Inference for robust canonical variate analysis},
  url          = {http://dx.doi.org/10.1007/s11634-010-0063-6},
  volume       = {4},
  year         = {2010},
}

Chicago
Van Aelst, Stefan, and Gert Willems. 2010. “Inference for Robust Canonical Variate Analysis.” Advances in Data Analysis and Classification 4 (2-3): 181–197.
APA
Van Aelst, S., & Willems, G. (2010). Inference for robust canonical variate analysis. ADVANCES IN DATA ANALYSIS AND CLASSIFICATION, 4(2-3), 181–197.
Vancouver
1.
Van Aelst S, Willems G. Inference for robust canonical variate analysis. ADVANCES IN DATA ANALYSIS AND CLASSIFICATION. 2010;4(2-3):181–97.
MLA
Van Aelst, Stefan, and Gert Willems. “Inference for Robust Canonical Variate Analysis.” ADVANCES IN DATA ANALYSIS AND CLASSIFICATION 4.2-3 (2010): 181–197. Print.