Parallel analysis approach for determining dimensionality in canonical correlation analysis

Canonical correlations are maximized correlation coefficients indicating the relationships between pairs of canonical variates that are linear combinations of the two sets of original variables. The number of non-zero canonical correlations in a population is called its dimensionality. Parallel anal...

Full description

Saved in:
Bibliographic Details
Published in:Journal of statistical computation and simulation 2016-11, Vol.86 (17), p.3419-3431
Main Authors: Golbasi Simsek, Guelhayat, Aydogdu, Selahattin
Format: Article
Language:English
Subjects:
bootstrap
Bootstrap method
Canonical correlations
Computation
Computer simulation
Correlation
Correlation analysis
dimensionality
Empirical analysis
Factor analysis
Mathematical models
parallel analysis
Population (statistical)
simulation
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Canonical correlations are maximized correlation coefficients indicating the relationships between pairs of canonical variates that are linear combinations of the two sets of original variables. The number of non-zero canonical correlations in a population is called its dimensionality. Parallel analysis (PA) is an empirical method for determining the number of principal components or factors that should be retained in factor analysis. An example is given to illustrate for adapting proposed procedures based on PA and bootstrap modified PA to the context of canonical correlation analysis (CCA). The performances of the proposed procedures are evaluated in a simulation study by their comparison with traditional sequential test procedures with respect to the under-, correct- and over-determination of dimensionality in CCA.
ISSN:0094-9655
1563-5163
DOI:10.1080/00949655.2016.1161044