The Matrix Approach to Logit Models

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


In this chapter we again discuss logit models, but here we use the matrix approach of Chapter VI. Section 1 discusses the equivalence of logit models and log-linear models. This equivalence is used to arrive at results on estimation and testing. Because the data in a typical logistic regression correspond to very sparse data in a contingency table, the asymptotic results of Section VI.2 are not appropriate. Section 2 presents results from Haberman (1977) that are appropriate for logistic regression models. Section 3 discusses model selection criteria for logistic regression. Direct fitting of logit models is considered in Section 4. The appropriate maximum likelihood equations and Newton-Raphson procedure are given. Section 5 indicates how the weighted least squares model fitting procedure is applied to logit models. Models appropriate for response variables with more than two categories are examined in Section 6. Finally, Section 7 considers the discrimination problem.


Logistic Regression Logistic Model Asymptotic Result Matrix Approach Case Variable 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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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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