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Selection of ordinally scaled independent variables with applications to international classification of functioning core sets
Author(s) -
Gertheiss Jan,
Hogger Sara,
Oberhauser Cornelia,
Tutz Gerhard
Publication year - 2011
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/j.1467-9876.2010.00753.x
Subject(s) - feature selection , ordinal data , context (archaeology) , core (optical fiber) , selection (genetic algorithm) , ordinal regression , variables , covariate , boosting (machine learning) , econometrics , variable (mathematics) , lasso (programming language) , ordinal scale , computer science , mathematics , machine learning , statistics , geography , telecommunications , mathematical analysis , world wide web , archaeology
Summary. Ordinal categorial variables arise commonly in regression modelling. Although the analysis of ordinal response variables has been well investigated, less work has been done concerning ordinal predictors. We consider so‐called international classfication of functioning core sets for chronic widespread pain, in which many ordinal covariates are collected. The effect of specific international classification of functioning variables on a subjective measure of physical health is investigated, which requires strategies for variable selection. In this context, we propose methods for the selection of ordinally scaled independent variables in the classical linear model. 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. Both methods are discussed in general, and applied to international classification of functioning core sets for chronic widespread pain.