2018/01/04 by Philip E. Cheng, Jiun-Wei Liou, Cheng, Philip E. +5
Chemistry · Computer Science · #94A17 #Bayesian Modeling and Causal Inference #Data Management and Algorithms #FOS: Computer and information sciences #History and advancements in chemistry #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.1801.01278
openalex publication_date 2018/01/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Association between categorical variables in contingency tables is analyzed using the information identities based on multivariate multinomial distributions. A scheme of geometric decompositions of the information identities is developed to identify indispensable predictors and interaction effects in the construction of concise log-linear and logit models; it suggests a new approach for selecting parsimonious log-linear and logit models which would facilitate the search for the minimum AIC models as a byproduct. The proposed constructive schemes are illustrated along with the analysis of a contingency data table collected in a study on the risk factors of ischemic cerebral stroke.