Maximum Likelihood Theory for Log-Linear Models

  • Ronald Christensen
Part of the Springer Texts in Statistics book series (STS)


This chapter presents the basic theoretical results of fitting log-linear models by maximum likelihood. The level of mathematical sophistication is considerably higher than in the rest of the book. The presentation assumes knowledge of advanced calculus, mathematical statistics, and large sample theory. Although the results in this chapter are proven in a different manner than for regular linear models, the results themselves are quite similar in nature. The common linear structure of the two techniques leads to the well-known analogies between them. A familiarity with log-linear models at the level of, say Fienberg (1980), is assumed.


Logit Model Asymptotic Property Asymptotic Distribution Saturated Model Moment Generate Function 


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Copyright information

© Springer Science+Business Media New York 1990

Authors and Affiliations

  • Ronald Christensen
    • 1
  1. 1.Department of Mathematics and StatisticsUniversity of New MexicoAlbuquerqueUSA

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