期刊
SCANDINAVIAN JOURNAL OF STATISTICS
卷 38, 期 2, 页码 197-214出版社
WILEY-BLACKWELL
DOI: 10.1111/j.1467-9469.2011.00740.x
关键词
adaptive Lasso; coordinate gradient descent; coordinatewise optimization; Lasso; random-effects model; variable selection; variance components
资金
- Swiss National Science Foundation [20PA21-120043/1]
- Swiss National Science Foundation (SNF) [20PA21-120043] Funding Source: Swiss National Science Foundation (SNF)
We propose an l(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.
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