Generalized canonical correlation analysis with missing values

Michel van de Velden and Y. Takane

No EI 2009-28, Econometric Institute Research Papers from Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute

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.

Keywords: generalized canoncial correlation analysis; missing values (search for similar items in EconPapers)
Date: 2009-11-02
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