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December 2010 Sparse modeling of categorial explanatory variables
Jan Gertheiss, Gerhard Tutz
Ann. Appl. Stat. 4(4): 2150-2180 (December 2010). DOI: 10.1214/10-AOAS355

Abstract

Shrinking methods in regression analysis are usually designed for metric predictors. In this article, however, shrinkage methods for categorial predictors are proposed. As an application we consider data from the Munich rent standard, where, for example, urban districts are treated as a categorial predictor. If independent variables are categorial, some modifications to usual shrinking procedures are necessary. Two L1-penalty based methods for factor selection and clustering of categories are presented and investigated. The first approach is designed for nominal scale levels, the second one for ordinal predictors. Besides applying them to the Munich rent standard, methods are illustrated and compared in simulation studies.

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Jan Gertheiss. Gerhard Tutz. "Sparse modeling of categorial explanatory variables." Ann. Appl. Stat. 4 (4) 2150 - 2180, December 2010. https://doi.org/10.1214/10-AOAS355

Information

Published: December 2010
First available in Project Euclid: 4 January 2011

zbMATH: 1220.62092
MathSciNet: MR2829951
Digital Object Identifier: 10.1214/10-AOAS355

Keywords: Categorial predictors , Fused lasso , ordinal predictors , rent standard , variable fusion

Rights: Copyright © 2010 Institute of Mathematical Statistics

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Vol.4 • No. 4 • December 2010
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