Multiple nonparametric regression and model validation for mixed regressors

URN to cite this document:: urn:nbn:de:bvb:355-epub-298096
DOI to cite this document:: 10.5283/epub.29809

Schnurbus, Joachim

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Date of publication of this fulltext: 14 May 2014 16:14

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Item type:	Thesis of the University of Regensburg (PhD)
Open Access Type:	Primary Publication
Date:	14 May 2014
Referee:	Prof. Dr. Rolf Tschernig and Prof. Dr. Harry Haupt
Date of exam:	6 July 2011
Institutions:	Business, Economics and Information Systems > Institut für Volkswirtschaftslehre und Ökonometrie > Lehrstuhl für Ökonometrie (Prof. Dr. Rolf Tschernig)
Keywords:	kernel regression, model validation, mixed covariates
Dewey Decimal Classification:	300 Social sciences > 330 Economics
Status:	Published
Refereed:	Yes, this version has been refereed
Created at the University of Regensburg:	Yes
Item ID:	29809

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Abstract (English)

The dissertation covers four essays on nonparametric (kernel and/or spline) regression, where tools and methods for the corresponding model validation are provided and discussed for a setup of mixed (discrete and continuous) covariates.

Translation of the abstract (German)

Die Dissertation umfasst vier Aufsätze zu nichtparametrischer (Kern- bzw. Spline-) Regression. Es werden Werkzeuge und Methoden zur Modellvalidierung in einem Setup von gemischten (diskreten und stetigen) Kovariablen aufgezeigt und diskutiert.

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