Adaptive Lasso for High Dimensional Regression and Gaussian Graphical Modeling
Statistics Theory
2009-03-17 v1 Machine Learning
Statistics Theory
Abstract
We show that the two-stage adaptive Lasso procedure (Zou, 2006) is consistent for high-dimensional model selection in linear and Gaussian graphical models. Our conditions for consistency cover more general situations than those accomplished in previous work: we prove that restricted eigenvalue conditions (Bickel et al., 2008) are also sufficient for sparse structure estimation.
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Cite
@article{arxiv.0903.2515,
title = {Adaptive Lasso for High Dimensional Regression and Gaussian Graphical Modeling},
author = {Shuheng Zhou and Sara van de Geer and Peter Bühlmann},
journal= {arXiv preprint arXiv:0903.2515},
year = {2009}
}
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30 pages