Template-Type: ReDIF-Paper 1.0
Author-Name: van de Velden, M.
Author-Name-Last: van de Velden
Author-Name-First: Michel
Author-Name: Takane, Y.
Author-Name-Last: Takane
Title: Generalized canonical correlation analysis with missing values
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.
Creation-Date: 2009-11-02
File-URL: https://repub.eur.nl/pub/17106/EI2009-28.pdf
File-Format: application/pdf
Series: RePEc:ems:eureir
Number: EI 2009-28
Keywords: generalized canoncial correlation analysis, missing values
Handle: RePEc:ems:eureir:17106