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Journal articles
Information-Based Parametrization of Log-Linear Models for Categorical Data Analysis
Abstract : Zighera (App Stoch Mod Data Anal 1:93–108 1985) introduced a new parameterization of log-linear models for analyzing categorical data, directly linked to a thorough analysis of discrimination information through Kullback-Leibler divergence. The method mainly aims at quantifying in terms of information the variations of a binary variable of interest, by comparing two contingency tables – or sub-tables – through effects of explanatory categorical variables. The present paper settles the mathematical background necessary to rigorously apply Zighera’s parameterization to any categorical data. In particular, identifiability and good properties of asymptotically χ 2-distributed test statistics are proven to hold. Determination of parameters and all tests of effects due to explanatory variables are simultaneous. Application to classical data sets illustrates contribution with respect to existing methods.
https://hal-normandie-univ.archives-ouvertes.fr/hal-02299589
Contributor : Valerie Girardin <>
Submitted on : Friday, September 27, 2019 - 6:35:11 PM
Last modification on : Monday, April 27, 2020 - 4:14:03 PM
Contributor : Valerie Girardin <>
Submitted on : Friday, September 27, 2019 - 6:35:11 PM
Last modification on : Monday, April 27, 2020 - 4:14:03 PM
