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• W4378515743 abstract "This paper proposed the use of mutual information (MI) decomposition as a novel approach to identifying indispensable variables and their interactions for contingency table analysis. The MI analysis identified subsets of associative variables based on multinomial distributions and validated parsimonious log-linear and logistic models. The proposed approach was assessed using two real-world datasets dealing with ischemic stroke (with 6 risk factors) and banking credit (with 21 discrete attributes in a sparse table). This paper also provided an empirical comparison of MI analysis versus two state-of-the-art methods in terms of variable and model selections. The proposed MI analysis scheme can be used in the construction of parsimonious log-linear and logistic models with a concise interpretation of discrete multivariate data." @default.
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• W4378515743 date "2023-05-04" @default.
• W4378515743 modified "2023-10-16" @default.
• W4378515743 title "Modeling Categorical Variables by Mutual Information Decomposition" @default.
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• W4378515743 doi "https://doi.org/10.3390/e25050750" @default.
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