Estimation for High-Dimensional Linear Mixed-Effects Models Using ℓ1-Penalization


  • This paper is based on a plenary presentation at NORDSTAT Meeting in Voss Norway, June 2010.

Jürg Schelldorfer, Seminar für Statistik, Department of Mathematics, ETH Zurich, CH-8092 Zurich, Switzerland.


Abstract.  We propose an ℓ1-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.