Analysis of contingency tables by ideal point discriminant analysis
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DOI: 10.1007/BF02294815
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References listed on IDEAS
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Cited by:
- Kamiya, Hidehiko & Takemura, Akimichi, 1997. "On Rankings Generated by Pairwise Linear Discriminant Analysis of m Populations," Journal of Multivariate Analysis, Elsevier, vol. 61(1), pages 1-28, April.
- Budiaji, Weksi, 2012. "Item Response Model from a Distance Perspective," Thesis Commons wcbu9, Center for Open Science.
- Mark Rooij, 2018. "Transitional modeling of experimental longitudinal data with missing values," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(1), pages 107-130, March.
- Kamiya, Hidehiko & Takemura, Akimichi, 2005. "Characterization of rankings generated by linear discriminant analysis," Journal of Multivariate Analysis, Elsevier, vol. 92(2), pages 343-358, February.
- Hailemichael M. Worku & Mark De Rooij, 2017. "Properties of Ideal Point Classification Models for Bivariate Binary Data," Psychometrika, Springer;The Psychometric Society, vol. 82(2), pages 308-328, June.
- Willem Heiser, 2004. "Geometric representation of association between categories," Psychometrika, Springer;The Psychometric Society, vol. 69(4), pages 513-545, December.
- Ulf Böckenholt & Ingo Böcknholt, 1990. "Canonical analysis of contingency tables with linear constraints," Psychometrika, Springer;The Psychometric Society, vol. 55(4), pages 633-639, December.
- Yoshio Takane & Haruo Yanai & Shinichi Mayekawa, 1991. "Relationships among several methods of linearly constrained correspondence analysis," Psychometrika, Springer;The Psychometric Society, vol. 56(4), pages 667-684, December.
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Keywords
Multidimensional scaling; model evaluation; AIC; the loglinear model; dual scaling (correspondence analysis); canonical analysis; the RC association model;All these keywords.
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