Square-root lasso: pivotal recovery of sparse signals via conic programming
We propose a pivotal method for estimating high-dimensional sparse linear regression models, where the overall number of regressors p is large, possibly much larger than n, but only s regressors are significant. The method is a modification of the lasso, called the square-root lasso. The method is p...
Main Authors: | Bellini, A., Chernozhukov, Victor V., Wang, Lie |
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Other Authors: | Massachusetts Institute of Technology. Department of Economics |
Format: | Article |
Language: | en_US |
Published: |
Oxford University Press
2012
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Online Access: | http://hdl.handle.net/1721.1/71663 https://orcid.org/0000-0003-3582-8898 https://orcid.org/0000-0002-3250-6714 |
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