Fitting longitudinal reduced-rank regression models by alternating least squares |
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| Authors: | Catrien C. J. H. Bijleveld Jan De Leeuw |
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| Affiliation: | (1) Department of Psychometrics, Leiden University, The Netherlands;(2) Departments of Psychology and Mathematics, University of California at Los Angeles, USA;(3) 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.Financial support by the Institute for Traffic Safety Research (SWOV) in Leidschendam, The Netherlands is gratefully acknowledged. |
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| Keywords: | reduced-rank regression state space analysis optimal scaling |
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