06075216

j

06075216 Schelldorfer, J\"urg B\"uhlmann, Peter van de Geer, Sara Estimation for high-dimensional linear mixed-effects models using $\ell_1$-penalization. Scand. J. Stat. 38, No. 2, 197-214 (2011). 2011 Wiley-Blackwell, Oxford EN adaptive lasso coordinate gradient descent coordinatewise optimization lasso random-effects model variable selection variance components

doi:10.1111/j.1467-9469.2011.00740.x

Summary: We propose an $\ell _{1}$-penalized estimation procedure for high-dimensional linear mixed-effects models. The models are useful whenever there is a grouping structure among high-dimensional observations, that is, for clustered data. We prove a consistency and an oracle optimality result and we develop an algorithm with provable numerical convergence. Furthermore, we demonstrate the performance of the method on simulated and a real high-dimensional data set.