Sparse Modeling of Categorial Explanatory Variables

By Jan Gertheiss, Gerhard E. Tutz. 2011

99 citations recorded by the source corpus.

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.

Subfields: Applied Mathematics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/01297918f44c3159ed50ae1874c759b31ccd8a82

Citations in the corpus

• Improving the External Validity of Conjoint Analysis: The Essential Role of Profile Distribution (2021, unknown): “Uncertainty estimates are calculated based on 5 Adaptive weights are defined as (Gertheiss and Tutz, 2010). in Ono and Burden (2019).”