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.

Erasmus School of Economics
Econometric Institute Research Papers
Report / Econometric Institute, Erasmus University Rotterdam
Erasmus School of Economics

van de Velden, M., & Takane, Y. (2009). Generalized canonical correlation analysis with missing values (No. EI 2009-28). Report / Econometric Institute, Erasmus University Rotterdam (pp. 1–21). Retrieved from