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

Oelker, Magret-Ruth; Gertheiss, Jan and Tutz, Gerhard (2014): Regularization and model selection with categorical predictors and effect modifiers in generalized linear models. In: Statistical Modeling, Vol. 14, No. 2: pp. 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.

• Item Type: Journal article
• Faculties: Mathematics, Computer Science and Statistics > Statistics; Mathematics, Computer Science and Statistics > Statistics > Chairs/Working Groups > Seminar for Applied Stochastic
• Subjects: 500 Science > 510 Mathematics
• Alliance/National Licence: This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.
• Language: English