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\iteman{io-port 06075216}
\itemau{Schelldorfer, J\"urg; B\"uhlmann, Peter; van de Geer, Sara}
\itemti{Estimation for high-dimensional linear mixed-effects models using $\ell_1$-penalization.}
\itemso{Scand. J. Stat. 38, No. 2, 197-214 (2011).}
\itemab
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.
\itemrv{~}
\itemcc{}
\itemut{adaptive lasso; coordinate gradient descent; coordinatewise optimization; lasso; random-effects model; variable selection; variance components}
\itemli{doi:10.1111/j.1467-9469.2011.00740.x}
\end