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A Numerical Approach to the Approximate and the Exact Minimum Rank of a Covariance Matrix

Published online by Cambridge University Press:  01 January 2025

Jos M. F. ten Berge*
Affiliation:
University of Groningen
Henk A. L. Kiers
Affiliation:
University of Groningen
*
Requests for reprints should be sent to Jos M. F. ten Berge, Vakgroep Psychologie, Rijksuniversiteit Groningen, Grote Kruisstraat 2/1, 9712 TS Groningen, THE NETHERLANDS.

Abstract

A concept of approximate minimum rank for a covariance matrix is defined, which contains the (exact) minimum rank as a special case. A computational procedure to evaluate the approximate minimum rank is offered. The procedure yields those proper communalities for which the unexplained common variance, ignored in low-rank factor analysis, is minimized. The procedure also permits a numerical determination of the exact minimum rank of a covariance matrix, within limits of computational accuracy. A set of 180 covariance matrices with known or bounded minimum rank was analyzed. The procedure was successful throughout in recovering the desired rank.

Type
Original Paper
Copyright
Copyright © 1991 The Psychometric Society

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Footnotes

The authors are obliged to Paul Bekker for stimulating and helpful comments.

References

Bekker, P. A., & de Leeuw, J. (1987). The rank of reduced dispersion matrices. Psychometrika, 52, 125135.CrossRefGoogle Scholar
Bentler, P. M., & Woodward, J. A. (1980). Inequalities among lower bounds to reliability: With applications to test construction and factor analysis. Psychometrika, 45, 249267.CrossRefGoogle Scholar
Della Riccia, G., & Shapiro, A. (1982). Minimum rank and minimum trace of covariance matrices. Psychometrika, 47, 443448.CrossRefGoogle Scholar
Eckart, C., & Young, G. (1936). The approximation of one matrix of another by lower rank. Psychometrika, 1, 211218.CrossRefGoogle Scholar
Shapiro, A. (1982). Rank-reducibility of a symmetric matrix and sampling theory of minimum trace factor analysis. Psychometrika, 47, 187199.CrossRefGoogle Scholar
Shapiro, A. (1982). Weighted minimum trace factor analysis. Psychometrika, 47, 243264.CrossRefGoogle Scholar
Takeuchi, K., Yanai, H., & Mukherjee, B. N. (1982). The foundations of multivariate analysis, New Delhi: Wiley.Google Scholar
ten Berge, J. M. F., & Kiers, H. A. L. (1988). Proper communality estimates minimizing the sum of the smallest eigenvalues for a covariance matrix. In Jansen, M. G. H. & van Schuur, W. H. (Eds.), The many faces of multivariate analysis. Proceedings of the SMABS-88 Conference in Gronigen (pp. 3037). Groningen: RION.Google Scholar
ten Berge, J. M. F., Snijders, T. A. B., & Zegers, F. E. (1981). Computational aspects of the greatest lower bound to reliability and constrained minimum trace factor analysis. Psychometrika, 46, 201213.CrossRefGoogle Scholar