Log-Linear Models: Estimation

  • Tamás Rudas
Part of the Springer Texts in Statistics book series (STS)


Maximum likelihood estimation of log-linear models is considered as a special case of maximum likelihood estimation in exponential families, with the mixed parameterization used in the definition of log-linear models playing a central role: the canonical parameters on the ascending class are specified by the model, while the marginal distributions on the descending class are taken from the observed data in the maximum likelihood estimates. Then, the main tool of computing maximum likelihood estimates, the Iterative Proportional Fitting Procedure, is described, and its convergence is proved.


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Tamás Rudas
    • 1
    • 2
  1. 1.Center for Social SciencesHungarian Academy of SciencesBudapestHungary
  2. 2.Eötvös Loránd UniversityBudapestHungary

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