ℓ1-penalization for mixture regression models-Reference-Cited by-同舟云学术

ℓ1-penalization for mixture regression models

Published:2010-06-30 Issue:2 Volume:19 Page:209-256
ISSN:1133-0686
Container-title:TEST
language:en
Short-container-title:TEST

Author:

Städler Nicolas,Bühlmann Peter,van de Geer Sara

Publisher

Springer Science and Business Media LLC

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

http://link.springer.com/content/pdf/10.1007/s11749-010-0197-z.pdf

Reference44 articles.

1. Bertsekas D (1995) Nonlinear programming. Athena Scientific, Belmont

2. Bickel P, Ritov Y, Tsybakov A (2009) Simultaneous analysis of Lasso and Dantzig selector. Ann Stat 37:1705–1732

3. Bunea F, Tsybakov A, Wegkamp M (2007) Sparsity oracle inequalities for the Lasso. Electron J Stat 1:169–194

4. Cai T, Wang L, Xu G (2009a) Stable recovery of sparse signals and an oracle inequality. Tech rep, Department of Statistics, University of Pennsylvania

5. Cai T, Xu G, Zhang J (2009b) On recovery of sparse signals via ℓ 1 minimization. IEEE Trans Inf Theory 55:3388–3397

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