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Some Boundary Conditions for a Monotone Analysis of Symmetric Matrices

Published online by Cambridge University Press:  01 January 2025

James C. Lingoes
James C. Lingoes*
Affiliation:
The University of Michigan
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Abstract

This paper gives a rigorous and greatly simplified proof of Guttman's theorem for the least upper-bound dimensionality of arbitrary real symmetric matrices S, where the points embedded in a real Euclidean space subtend distances which are strictly monotone with the off-diagonal elements of S. A comparable and more easily proven theorem for the vector model is also introduced. At most n-2 dimensions are required to reproduce the order information for both the distance and vector models and this is true for any choice of real indices, whether they define a metric space or not. If ties exist in the matrices to be analyzed, then greatest lower bounds are specifiable when degenerate solutions are to be avoided. These theorems have relevance to current developments in nonmetric techniques for the monotone analysis of data matrices.

Keywords

Boundary ConditionLower BoundPublic PolicyEuclidean SpaceStatistical Theory

Information

Type
Original Paper
Information
Psychometrika , Volume 36 , Issue 2 , June 1971 , pp. 195 - 203
Copyright
Copyright © 1971 The Psychometric Society

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Footnotes

*

This research in nonmetric methods is supported in part by a grant from the National Science Foundation (GS-929 & -2850).

The very helpful comments and encouragement of Louis Guttman and J. Douglas Carroll are greatly appreciated. Finally, to that unknown, but not unsung, reviewer who helped in the clarification of the argument, I express my thanks.

References

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