Regularization for Generalized Additive Mixed Models by Likelihood-Based Boosting

Groll, Andreas and Tutz, Gerhard (2012): Regularization for Generalized Additive Mixed Models by Likelihood-Based Boosting. In: Methods of Information in Medicine, Vol. 51, No. 2: pp. 168-77

Abstract

With the emergence of semi- and nonparametric regression the generalized linear mixed model has been extended to account for additive predictors. However, available fitting methods fail in high dimensional settings where many explanatory variables are present. We extend the concept of boosting to generalized additive mixed models and present an appropriate algorithm that uses two different approaches for the fitting procedure of the variance components of the random effects.

• Item Type: Journal article
• Faculties: Mathematics, Computer Science and Statistics > Statistics; Mathematics, Computer Science and Statistics > Statistics > Chairs/Working Groups > Seminar for Applied Stochastic; Mathematics, Computer Science and Statistics > Mathematics > Workgroup Financial Mathematics
• Subjects: 500 Science > 510 Mathematics
• Language: English