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Nonparametric approaches to generalized linear models

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  • HÄRDLE, Wolfgang
  • TURLACH, Berwin

Abstract

In this paper we investigate the gains of using nonparametric estimation methods in a family of models related to Generalised Linear Models. We focus especially on discrete choice models. We give an overview on different nonparametric and semiparametric approaches in this setting. In particular we discuss estimation methods such as average derivative estimation (ADE), semiparametric weighted least squares (Single Index Models, SIM), Projection Pursuit Regression (PPR) and Generalized Additive Models (GAM). Their performance in practice and theory is compared.

Suggested Citation

  • HÄRDLE, Wolfgang & TURLACH, Berwin, 1992. "Nonparametric approaches to generalized linear models," LIDAM Discussion Papers CORE 1992037, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvco:1992037
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    File URL: https://sites.uclouvain.be/core/publications/coredp/coredp1992.html
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    Cited by:

    1. Müller, Marlene, 1997. "Computer-assisted generalized partial linear models," SFB 373 Discussion Papers 1997,48, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.

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