Generalized linear mixed models based on boosting

Tutz, Gerhard and Groll, Andreas (2010): Generalized linear mixed models based on boosting. In: Kneib, Thomas (ed.) : Statistical Modelling and Regression Structures - Festschrift in Honour of Ludwig Fahrmeir. Heidelberg: Physica-Verlag, Springer. pp. 197-215

Abstract

A likelihood-based boosting approach for fitting generalized linear mixed models is presented. In contrast to common procedures it can be used in highdimensional settings where a large number of potentially influential explanatory variables is available. Constructed as a componentwise boosting method it is able to perform variable selection with the complexity of the resulting estimator being determined by information criteria. The method is investigated in simulation studies and illustrated by using a real data set.

• Item Type: Book Section
• 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
• ISBN: 978-3-7908-2412-4 ; 978-3-7908-2898-6 ; 978-3-7908-2413-1
• Place of Publication: Heidelberg
• Annotation: First Online: 29 December 2009
• Language: English