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

被引：2

作者：

Lu, Zhenqiu ^{[1
]}

机构：

[1] Univ Georgia, 325V Aderhold Hall,110 Carlton St, Athens, GA 30602 USA

来源：

QUANTITATIVE PSYCHOLOGY | 2019年 / 265卷

关键词：

Canonical correlation analysis; Structural equation modeling; Missing values; SEM;

D O I：

10.1007/978-3-030-01310-3_22

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

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.

引用

页码：243 / 254

页数：12

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