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Generalized canonical correlation analysis with missing values

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  • van de Velden, M.
  • Takane, Y.

Abstract

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 Gifi's homogeneity analysis framework, and the GENCOM algorithm developed by Green and Carroll.

Suggested Citation

  • van de Velden, M. & Takane, Y., 2009. "Generalized canonical correlation analysis with missing values," Econometric Institute Research Papers EI 2009-28, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:17106
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    References listed on IDEAS

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    1. Paul Horst, 1961. "Relations amongm sets of measures," Psychometrika, Springer;The Psychometric Society, vol. 26(2), pages 129-149, June.
    2. Michel Velden & Tammo Bijmolt, 2006. "Generalized canonical correlation analysis of matrices with missing rows: a simulation study," Psychometrika, Springer;The Psychometric Society, vol. 71(2), pages 323-331, June.
    3. Eeke Burg & Jan Leeuw & Renée Verdegaal, 1988. "Homogeneity analysis withk sets of variables: An alternating least squares method with optimal scaling features," Psychometrika, Springer;The Psychometric Society, vol. 53(2), pages 177-197, June.
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