Selection of Ordinally Scaled Independent Variables

Gertheiss, Jan; Hogger, Sara; Oberhauser, Cornelia and Tutz, Gerhard (July 2009): Selection of Ordinally Scaled Independent Variables. Department of Statistics: Technical Reports, No.62 Applied Statistics (accepted for publication)

Abstract

Ordinal categorial variables are a common case in regression modeling. Although the case of ordinal response variables has been well investigated, less work has been done concerning ordinal predictors. This article deals with the selection of ordinally scaled independent variables in the classical linear model, where the ordinal structure is taken into account by use of a difference penalty on adjacent dummy coefficients. It is shown how the Group Lasso can be used for the selection of ordinal predictors, and an alternative blockwise Boosting procedure is proposed. Emphasis is placed on the application of the presented methods to the (Comprehensive) ICF Core Set for chronic widespread pain.

The paper is a preprint of an article accepted for publication in the Journal of the Royal Statistical Society Series C (Applied Statistics). Please use the journal version for citation.

• Item Type: Paper
• Form of publication: Postprint
• Keywords: Boosting, ICF Core Sets, Lasso, Ordinal Predictors, Ridge, Variable Selection
• Faculties: Mathematics, Computer Science and Statistics > Statistics > Technical Reports
• Subjects: 600 Technology > 610 Medicine and health
• Language: English