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1990
Consider a three-way contingency table {Xijk, i=1,...I, j=1,...,J, k=1,...,K}. As model for such data, it may be assumed that the x’s are observed values of random variables Xijk, i=1,...,I, j=1,...,J, k=1,...,K with a multinomial distribution $${X_{111}},...,{X_{IJK}} \sim M(n,{p_{111}},...,{p_{IJK}}).$$ (5.1)
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Consider a three-way contingency table {Xijk, i=1,...I, j=1,...,J, k=1,...,K}. As model for such data, it may be assumed that the x’s are observed values of random variables Xijk, i=1,...,I, j=1,...,J, k=1,...,K with a multinomial distribution $${X_{111}},...,{X_{IJK}} \sim M(n,{p_{111}},...,{p_{IJK}}).$$ (5.1)
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Latent Structure Analysis of a Set of Multidimensional Contingency Tables
Journal of the American Statistical Association, 1984Leo A Goodman
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A Test for Detecting Outlying Cells in the Multinomial Distribution and Two-Way Contingency Tables
Journal of the American Statistical Association, 1980Ron S Kenett
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Default “Gunel and Dickey” Bayes factors for contingency tables
Behavior Research Methods, 2016Tahira Jamil +2 more
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