Nonparametric 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 8 in Regression, 2013, pp 413-533 from Springer

Abstract: Abstract The main goal of nonparametric regression is the flexible modeling of effects of continuous covariates on a dependent variable. We have already seen in several practical applications that a purely linear model is not always sufficient. This insufficiency could either result from theoretical considerations about the given application or simply from uncertainty about the specific form of an effect that a covariate has on the response.

Keywords: Markov Random Field; Smoothing Parameter; Nonparametric Regression; Thin Plate Spline; Polynomial Spline (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-34333-9_8

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DOI: 10.1007/978-3-642-34333-9_8

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