Adaptive Lasso for High Dimensional Regression and Gaussian Graphical Modeling

Shuheng Zhou; Sara van de Geer; Peter Bühlmann

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arXiv · 0903.2515

Adaptive Lasso for High Dimensional Regression and Gaussian Graphical Modeling

Shuheng Zhou·Sara van de Geer·Peter Bühlmann

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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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Shuheng Zhou, Sara van de Geer, Peter Bühlmann. 2009-03-13. Adaptive Lasso for High Dimensional Regression and Gaussian Graphical Modeling. https://arxiv.org/abs/0903.2515

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