Canonical analysis of two convex polyhedral cones and applications
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DOI: 10.1007/BF02294404
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References listed on IDEAS
- Forrest Young & Yoshio Takane & Jan Leeuw, 1978. "The principal components of mixed measurement level multivariate data: An alternating least squares method with optimal scaling features," Psychometrika, Springer;The Psychometric Society, vol. 43(2), pages 279-281, June.
- Forrest Young, 1981. "Quantitative analysis of qualitative data," Psychometrika, Springer;The Psychometric Society, vol. 46(4), pages 357-388, December.
- R. Bradley & S. Katti & Irma Coons, 1962. "Optimal scaling for ordered categories," Psychometrika, Springer;The Psychometric Society, vol. 27(4), pages 355-374, December.
- Forrest Young & Jan Leeuw & Yoshio Takane, 1976. "Regression with qualitative and quantitative variables: An alternating least squares method with optimal scaling features," Psychometrika, Springer;The Psychometric Society, vol. 41(4), pages 505-529, December.
- J. Kruskal, 1964. "Nonmetric multidimensional scaling: A numerical method," Psychometrika, Springer;The Psychometric Society, vol. 29(2), pages 115-129, June.
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Cited by:
- Krijnen, Wim P., 1996. "Algorithms for unweighted least-squares factor analysis," Computational Statistics & Data Analysis, Elsevier, vol. 21(2), pages 133-147, February.
- Olivier Gascuel & Denise Levy, 1996. "A reduction algorithm for approximating a (nonmetric) dissimilarity by a tree distance," Journal of Classification, Springer;The Classification Society, vol. 13(1), pages 129-155, March.
- Tenenhaus, Michel & Vinzi, Vincenzo Esposito & Chatelin, Yves-Marie & Lauro, Carlo, 2005. "PLS path modeling," Computational Statistics & Data Analysis, Elsevier, vol. 48(1), pages 159-205, January.
- Shizuhiko Nishisato, 1996. "Gleaning in the field of dual scaling," Psychometrika, Springer;The Psychometric Society, vol. 61(4), pages 559-599, December.
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Keywords
convex polyhedral cone; alternating least squares algorithm; optimal scaling; monotone analysis of variance; optimal multiple regression;All these keywords.
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