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Generalized canonical correlation analysis with missing values

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Author Info

  • Michel Velden

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  • Yoshio Takane

    ()

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    Abstract

    Generalized canonical correlation analysis is a versatile technique that allows the joint analysis of several sets of data matrices. The generalized canonical correlation analysis solution can be obtained through an eigenequation and distributional assumptions are not required. When dealing with multiple set data, the situation frequently occurs that some values are missing. In this paper, two new methods for dealing with missing values in generalized canonical correlation analysis are introduced. The first approach, which does not require iterations, is a generalization of the Test Equating method available for principal component analysis. In the second approach, missing values are imputed in such a way that the generalized canonical correlation analysis objective function does not increase in subsequent steps. Convergence is achieved when the value of the objective function remains constant. By means of a simulation study, we assess the performance of the new methods. We compare the results with those of two available methods; the missing-data passive method, introduced in Gifi’s homogeneity analysis framework, and the GENCOM algorithm developed by Green and Carroll. An application using world bank data is used to illustrate the proposed methods. Copyright The Author(s) 2012

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    File URL: http://hdl.handle.net/10.1007/s00180-011-0276-y
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    Bibliographic Info

    Article provided by Springer in its journal Computational Statistics.

    Volume (Year): 27 (2012)
    Issue (Month): 3 (September)
    Pages: 551-571

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    Handle: RePEc:spr:compst:v:27:y:2012:i:3:p:551-571

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    Web page: http://www.springerlink.com/link.asp?id=120306

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    Related research

    Keywords: Generalized canonical correlation analysis; Missing values;

    References

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    1. Zanakis, Stelios H. & Alvarez, Cecilia & Li, Vivian, 2007. "Socio-economic determinants of HIV/AIDS pandemic and nations efficiencies," European Journal of Operational Research, Elsevier, vol. 176(3), pages 1811-1838, February.
    2. Michel Velden & Tammo Bijmolt, 2006. "Generalized canonical correlation analysis of matrices with missing rows: a simulation study," Psychometrika, Springer, vol. 71(2), pages 323-331, June.
    3. Casper Albers & John Gower, 2010. "A general approach to handling missing values in Procrustes analysis," Advances in Data Analysis and Classification, Springer, vol. 4(4), pages 223-237, December.
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    Cited by:
    1. Kohei Adachi, 2013. "Generalized joint Procrustes analysis," Computational Statistics, Springer, vol. 28(6), pages 2449-2464, December.

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