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Robust Procedures in Multivariate Analysis II. Robust Canonical Variate Analysis

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  • N. A. Campbell

Abstract

Robust M‐estimation for canonical variate analysis is developed, based on a functional relationship model; the associated weights depend on the distance of an observation from the canonical variate mean for the group. For uncontaminated data, the robust M‐estimation procedure performs similarly to the usual canonical variate analysis. A typical data set is examined; the usual canonical vectors are little affected by the presence of atypical observations, though the canonical roots are considerably influenced.

Suggested Citation

  • N. A. Campbell, 1982. "Robust Procedures in Multivariate Analysis II. Robust Canonical Variate Analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 31(1), pages 1-8, March.
    • Handle: RePEc:bla:jorssc:v:31:y:1982:i:1:p:1-8
    DOI: 10.2307/2347068
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    Cited by:

    1. Pires, Ana M. & Branco, João A., 2010. "Projection-pursuit approach to robust linear discriminant analysis," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2464-2485, November.
    2. Sajobi, Tolulope T. & Lix, Lisa M. & Dansu, Bolanle M. & Laverty, William & Li, Longhai, 2012. "Robust descriptive discriminant analysis for repeated measures data," Computational Statistics & Data Analysis, Elsevier, vol. 56(9), pages 2782-2794.
    3. Kosinski, Andrzej S., 1998. "A procedure for the detection of multivariate outliers," Computational Statistics & Data Analysis, Elsevier, vol. 29(2), pages 145-161, December.
    4. Peter Verboon & Ivo Lans, 1994. "Robust canonical discriminant analysis," Psychometrika, Springer;The Psychometric Society, vol. 59(4), pages 485-507, December.
    5. Ke-Hai Yuan & Peter Bentler & Wai Chan, 2004. "Structural equation modeling with heavy tailed distributions," Psychometrika, Springer;The Psychometric Society, vol. 69(3), pages 421-436, September.
    6. Kamiya, Hidehiko & Eguchi, Shinto, 2001. "A Class of Robust Principal Component Vectors," Journal of Multivariate Analysis, Elsevier, vol. 77(2), pages 239-269, May.

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