TY - JOUR
T1 - Multivariate least squares and its relation to other multivariate techniques
AU - Lipovetsky, Stan
AU - Tishler, Asher
AU - Conklin, W. Michael
PY - 2002/10
Y1 - 2002/10
N2 - 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.
AB - 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.
KW - Canonical correlations
KW - Inter-battery factor analysis
KW - Multivariate least squares
KW - Partial least squares
KW - Principal components
KW - Robust canonical correlations
UR - http://www.scopus.com/inward/record.url?scp=0036818658&partnerID=8YFLogxK
U2 - 10.1002/asmb.462
DO - 10.1002/asmb.462
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AN - SCOPUS:0036818658
VL - 18
SP - 347
EP - 356
JO - Applied Stochastic Models in Business and Industry
JF - Applied Stochastic Models in Business and Industry
SN - 1524-1904
IS - 4
ER -