Canonical Correlation Analysis with Missing Values: A Structural Equation Modeling Approach

  • Zhenqiu (Laura) LuEmail author
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 265)


Canonical correlation analysis (CCA) is a generalization of multiple correlation that examines the relationship between two sets of variables. When there are missing values, spectral decomposition in CCA becomes complicated and difficult to implement. This article investigates structural equation modeling approach to Canonical correlation analysis when data have missing values.


Canonical correlation analysis Structural equation modeling Missing values 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of GeorgiaAthensUSA

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