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Multivariate Analysis with Linearizable Regressions

Published online by Cambridge University Press:  01 January 2025

Jan de Leeuw
Jan de Leeuw*
Affiliation:
Departments of Psychology and Mathematics, University of California, Los Angeles
*
Requests for reprints should be sent to Jan de Leeuw, Department of Psychology and Mathematics, University of California, Los Angeles, Los Angeles, CA 90024-1563.
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Abstract

We study the class of multivariate distributions in which all bivariate regressions can be linearized by separate transformation of each of the variables. This class seems more realistic than the multivariate normal or the elliptical distributions, and at the same time its study allows us to combine the results from multivariate analysis with optimal scaling and classical multivariate analysis. In particular a two-stage procedure which first scales the variables optimally, and then fits a simultaneous equations model, is studied in detail and is shown to have some desirable properties.

Keywords

multivariate analysisoptimal scalingcorrespondence analysisstructural modelssimultaneous equationsfactor analysisLISRELtransformation
Type
Original Paper
Information
Psychometrika , Volume 53 , Issue 4 , December 1988 , pp. 437 - 454
Copyright
Copyright © 1988 The Psychometric Society

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