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Models for Categorical Response Variables

  • Helge Toutenburg
  • Shalabh
Chapter
Part of the Springer Texts in Statistics book series (STS)

Abstract

Generalized linear models (GLMs) are a generalization of the classical linear models of regression analysis and analysis of variance, which model the relationship between the expectation of a response variable and unknown predictor variables according to
$$\begin{array}{ll} {\rm E}(y_i) &= x_{i1}\beta_1 + \ldots + x_{ip}\beta_p\\ &= x^{\prime}_i \beta.\\ \end{array}$$
(8.1)
The parameters are estimated according to the principle of least squares and are optimal according to the minimum dispersion theory or, in the case of a normal distribution, are optimal according to the ML theory (cf. Chapter 3).

Keywords

Contingency Table Saturated Model Binary Response Loglinear Model Independence Model 
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, LLC 2010

Authors and Affiliations

  1. 1.Institut für StatistikLudwig-Maximilians-UniversitätMünchenGermany
  2. 2.Department of Mathematics & StatisticsIndian Institute of TechnologyKanpurIndia

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