Adaptive Lasso for High Dimensional Regression and Gaussian Graphical Modeling

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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.
30 pages

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Statistics Theory, Machine Learning

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http://salesiana.dossiersoluciones.com/handle/123456789/223373

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/0903.2515
 http://arxiv.org/abs/0903.2515

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arXiv