A fast algorithm for iterative proportional fitting in log-linear models
Computational Statistics & Data Analysis
Discrete Applied Mathematics
Computing the maximum-entropy extension of given discrete probability distributions
Computational Statistics & Data Analysis
Elements of information theory
Elements of information theory
On the effective implementation of the iterative proportional fitting procedure
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
An implementation of the iterative proportional fitting procedure by propagation trees
Computational Statistics & Data Analysis
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We propose iterative proportional scaling (IPS) via decomposable submodels for maximizing the likelihood function of a hierarchical model for contingency tables. In ordinary IPS the proportional scaling is performed by cycling through the members of the generating class of a hierarchical model. We propose the adjustment of more marginals at each step. This is accomplished by expressing the generating class as a union of decomposable submodels and cycling through the decomposable models. We prove the convergence of our proposed procedure, if the amount of scaling is adjusted properly at each step. We also analyze the proposed algorithms around the maximum likelihood estimate (MLE) in detail. The faster convergence of our proposed procedure is illustrated by numerical examples.