A Constructive Procedure for Modeling Categorical Variables: Log-Linear and Logit Models
Abstract
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.
Cite
@article{arxiv.1801.01278,
title = {A Constructive Procedure for Modeling Categorical Variables: Log-Linear and Logit Models},
author = {Philip E. Cheng and Jiun-Wei Liou and Hung-Wen Kao and Michelle Liou},
journal= {arXiv preprint arXiv:1801.01278},
year = {2018}
}
Comments
This article sets up a new construction methodology for selecting the most parsimonious log-linear and logit models in any finite-dimensional categorical data table using the analysis of information identity. Please refer this article to arXiv: 1801.01003 [stat.ME] and Cheng et al. (JASA, 2010). Email your comments and questions to the corresponding author