Quantile Regression

Ludwig Fahrmeir, Thomas Kneib, Stefan Lang and Brian Marx
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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

Chapter 10 in Regression, 2013, pp 597-620 from Springer

Abstract: 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.

Keywords: Quantile Regression; Inverse Gaussian Distribution; Quantile Regression Model; Bibliographic Note; Implicit Definition (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/978-3-642-34333-9_10

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