Sparse Modeling of Categorial Explanatory Variables

Gertheiss, Jan and Tutz, Gerhard (2009): Sparse Modeling of Categorial Explanatory Variables. Department of Statistics: Technical Reports, No.60

Abstract

Shrinking methods in regression analysis are usually designed for metric predictors. If independent variables are categorial some modifications are necessary. In this article 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. All methods are illustrated and compared in simulation studies, and applied to real world data from the Munich rent standard. The paper is a preprint of an article published in The Annals of Applied Statistics. Please use the journal version for citation.

• Item Type: Paper (Technical Report)
• Published in: The Annals of Applied Statistics, No. 4, Vol. 4, 2010: pp. 2150-2180.
• Keywords: Fused Lasso, Variable Fusion, Categorial Predictors, Ordinal Predictors
• Subjects: Mathematics, Computer Science and Statistics > Statistics > Technical Reports
• Dewey Classification: 300 Social sciences > 310 General statistics; 600 Natural sciences and mathematics > 510 Mathematics
• Language: English