Nonparametric Approaches to Generalized Linear Models

  • Wolfgang K. Härdle
  • Berwin A. Turlach
Part of the Lecture Notes in Statistics book series (LNS, volume 78)


In this paper we consider classes of statistical models that are natural generalizations of generalized linear models. Generalized linear models cover a very broad class of classical statistical models including linear regression, ANOVA, logit, and probit models. An important element of generalized linear models is that they contain parametric components of which the influence has to be determined by the experimentator. Here we describe some lines of thought and research relaxing the parametric structure of these components.


Generalize Linear Model American Statistical Association Average Derivative Linear Predictor Projection Pursuit 
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-Verlag New York, Inc. 1992

Authors and Affiliations

  • Wolfgang K. Härdle
    • 1
  • Berwin A. Turlach
    • 1
  1. 1.C.O.R.E. and Institut de StatistiqueUniversité Catholique de LouvainLouvain-la-NeuveBelgium

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