Likelihood-Based Boosting in Binary and Ordinal Random Effects Models

This article presents a likelihood-based boosting approach for fitting binary and ordinal mixed models. In contrast to common procedures, this approach can be used in high-dimensional settings where a large number of potentially influential explanatory variables are 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 both for cumulative and sequential models and is illustrated by using real datasets. The supplementary materials for the article are available online.