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Fitting Longitudinal Reduced-Rank Regression Models by Alternating Least Squares

Published online by Cambridge University Press:  01 January 2025

Catrien C. J. H. Bijleveld  and
Jan De Leeuw
Catrien C. J. H. Bijleveld*
Affiliation:
Department of Psychometrics, Leiden University
Jan De Leeuw
Affiliation:
Departments of Psychology and Mathematics, University of California at Los Angeles
*
Requests for reprints should be sent to C. Bijleveld, Department of Psychometrics, Faculty of Social Sciences, P. O. Box 9555, 2300 RB Leiden, THE NETHERLANDS.
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Abstract

An alternating least squares method for iteratively fitting the longitudinal reduced-rank regression model is proposed. The method uses ordinary least squares and majorization substeps to estimate the unknown parameters in the system and measurement equations of the model. In an example with cross-sectional data, it is shown how the results conform closely to results from eigenanalysis. Optimal scaling of nominal and ordinal variables is added in a third substep, and illustrated with two examples involving cross-sectional and longitudinal data.

Keywords

reduced-rank regressionstate space analysisoptimal scaling
Type
Original Paper
Information
Psychometrika , Volume 56 , Issue 3 , September 1991 , pp. 433 - 447
Copyright
Copyright © 1991 The Psychometric Society

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Footnotes

Financial support by the Institute for Traffic Safety Research (SWOV) in Leidschendam, The Netherlands is gratefully acknowledged.

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