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Multivariate least squares and its relation to other multivariate techniques

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  • Stan Lipovetsky
  • Asher Tishler
  • W. Michael Conklin

Abstract

We consider multivariate least squares (LS) for the estimation of the connection between two data sets and show how LS is related to other multivariate techniques regularly used to analyse large data sets. LS methods are shown to be equivalent, or similar, to principal components, canonical correlations and its modifications, including a variant of the partial LS. LS approach provides a convenient unified framework for a general description and comparison of various multivariate methods, facilitates their understanding, and helps to identify their usefulness for various real‐world applications. As an example we estimate and discuss the relations between data sets containing managerial variables and success measures. Copyright © 2002 John Wiley & Sons, Ltd.

Suggested Citation

  • Stan Lipovetsky & Asher Tishler & W. Michael Conklin, 2002. "Multivariate least squares and its relation to other multivariate techniques," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 18(4), pages 347-356, October.
    • Handle: RePEc:wly:apsmbi:v:18:y:2002:i:4:p:347-356
    DOI: 10.1002/asmb.462
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    Cited by:

    1. Stan Lipovetsky, 2022. "Canonical Concordance Correlation Analysis," Mathematics, MDPI, vol. 11(1), pages 1-12, December.

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