%0 Journal Article %1 schelldorfer_estimation_2011 %A Schelldorfer, Jürg %A Bühlmann, Peter %A De Geer, Sara Van %D 2011 %J Scandinavian Journal of Statistics %K LASSO, Lasso, adaptive components coordinate coordinatewise descent, effects, gradient optimization, random selection, variable variance %N 2 %P 197--214 %R 10.1111/j.1467-9469.2011.00740.x %T Estimation for high-dimensional linear mixed-effects models using ℓ1-penalization %U http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9469.2011.00740.x/abstract %V 38 %X 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.

@article{schelldorfer_estimation_2011, abstract = {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.}, added-at = {2017-01-09T13:57:26.000+0100}, author = {Schelldorfer, Jürg and Bühlmann, Peter and De Geer, Sara Van}, biburl = {https://www.bibsonomy.org/bibtex/2b7bd63c37db747a9ceae6cffd0896004/yourwelcome}, copyright = {© 2011 Board of the Foundation of the Scandinavian Journal of Statistics}, doi = {10.1111/j.1467-9469.2011.00740.x}, interhash = {12fa8dce3272481e8d5453fceb7a113d}, intrahash = {b7bd63c37db747a9ceae6cffd0896004}, issn = {1467-9469}, journal = {Scandinavian Journal of Statistics}, keywords = {LASSO, Lasso, adaptive components coordinate coordinatewise descent, effects, gradient optimization, random selection, variable variance}, language = {en}, number = 2, pages = {197--214}, timestamp = {2017-01-09T14:01:11.000+0100}, title = {Estimation for high-dimensional linear mixed-effects models using ℓ1-penalization}, url = {http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9469.2011.00740.x/abstract}, urldate = {2013-06-25}, volume = 38, year = 2011 }