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Quantile Regression

In: Regression

Author

Listed:
  • Ludwig Fahrmeir

    (University of Munich, Department of Statistics)

  • Thomas Kneib

    (University of Göttingen, Chair of Statistics)

  • Stefan Lang

    (University of Innsbruck, Department of Statistics)

  • Brian Marx

    (Louisiana State University, Experimental Statistics)

Abstract

Essentially all regression models that we have dealt with thus far have been mean regression models since they relate the predictor η of a regression model to only one specific quantity of the response y, namely the expected value. For example, in case of a generalized linear model (or its extensions) with predictor η, we have $$E(y) = h(\eta ),$$ where h is a known response function. The distribution of the response was then, depending on this mean parameter, completely characterized (sometimes up to a scale parameter common to all observations and potentially with some prespecified weights) by the regression model.

Suggested Citation

  • Ludwig Fahrmeir & Thomas Kneib & Stefan Lang & Brian Marx, 2013. "Quantile Regression," Springer Books, in: Regression, edition 127, chapter 10, pages 597-620, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-34333-9_10
    DOI: 10.1007/978-3-642-34333-9_10
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