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Visualizing Contingency Tables
2007Categorical data analysis is typically based on two- or higher dimensional contingency tables, cross-tabulating the co-occurrences of levels of nominal and/or ordinal data. In order to explain these, statisticians typically look for (conditional) independence structures using common methods such as independence tests and log-linear models.
Meyer, David +2 more
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Journal of Biopharmaceutical Statistics, 2015
Jyh-Jiuan Lin, Ching-Hui Chang, N. Pal
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Jyh-Jiuan Lin, Ching-Hui Chang, N. Pal
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Full Contingency Tables, Logits, and Split Contingency Tables
Biometrics, 1969Three methods of fitting log-linear models to multivariate contingency-table data with one dichotomous variable are discussed. Logit analysis is commonly used when a full contingency table of s dimensions is regarded as a table of rates of dimension s -1. The split-table method treats the same data as two separate tables each of dimension s -1. We show
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Quantity, exchange, and shift components of difference in a square contingency table
, 2014R. Pontius, A. Santacruz
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Multi-dimensional Contingency Tables
1990In chapter 5, log-linear models for three-dimensional tables were treated in great details. Hence we shall not for higher order tables go into details with the parameterizations of the models or with the exact expressions for test quantities and their distributions.
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Privacy, accuracy, and consistency too: a holistic solution to contingency table release
ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, 2007B. Barak +5 more
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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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