Regularization and model selection with categorical predictors and effect modifiers in generalized linear models

Oelker, Magret-Ruth; Gertheiss, Jan und Tutz, Gerhard (2014): Regularization and model selection with categorical predictors and effect modifiers in generalized linear models. In: Statistical Modeling, Bd. 14, Nr. 2: S. 157-177

Abstract

Varying-coefficient models with categorical effect modifiers are considered within the framework of generalized linear models. We distinguish between nominal and ordinal effect modifiers, and propose adequate Lasso-type regularization techniques that allow for (1) selection of relevant covariates, and (2) identification of coefficient functions that are actually varying with the level of a potentially effect modifying factor. For computation, a penalized iteratively reweighted least squares algorithm is presented. We investigate large sample properties of the penalized estimates; in simulation studies, we show that the proposed approaches perform very well for finite samples, too. In addition, the presented methods are compared with alternative procedures, and applied to real-world data.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Mathematik, Informatik und Statistik > Statistik ; Mathematik, Informatik und Statistik > Statistik > Lehrstühle/Arbeitsgruppen > Seminar für angewandte Stochastik
• Themengebiete: 500 Naturwissenschaften und Mathematik > 510 Mathematik
• Allianz-/Nationallizenz: Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.
• Sprache: Englisch