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We describe a simple, efficient, permutation based procedure for selecting the penalty parameter in the LASSO.
Regression shrinkage and selection via lasso
Tibshirani R. 1996 · 1996
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Learning to remove Internet advertisements. ACM Press
Kushmerick N. 1999 · 1999
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Variable selection via nonconcave penalized likelihood and its oracle properties
Fan J, Li R. 2001 · 2001
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Rank-Extreme Association of Gaussian Vectors and Low-Rank Detection
Zhang K. 2013 · 2008
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Mapping in structured populations by resample model averaging
Valdar W, Holmes CC, Mott R, Flint J. 2009 · 2009
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SNP Selection in genome-wide and candidate gene studies via penalized logistic regression
Ayers KL, Cordell HJ. 2010 · 2010
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Regularization paths for generalized linear models via coordinate descent
Friedman J, Hastie T, Tibshirani R. 2010 · 2010
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Stability selection
Meinshausen N, Bühlmann P. 2010 · 2010
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Statistics for High-Dimensional Data - Methods, Theory and Applications
Bühlmann P, van de Geer S. 2011 · 2011
Cited alongside, same era.
Regression shrinkage and selection via the lasso: a retrospective
Tibshirani R. 2011 · 2011
Cited alongside, same era.
Comprehensive molecular portraits of human breast tumours
TCGA. 2012 · 2012
Cited alongside, same era.
Reprioritizing genetic associations in hit regions using lasso-based resample model averaging
Valdar W, Sabourin J, Nobel A, Holmes CC. 2012 · 2012
Later among the works it cites.
Consistent Cross-Validation for Tuning Parameter Selection in High-Dimensional Variable Selection
Feng Y, Yu Y. 2013 · 2013
Later among the works it cites.
How correlations influence lasso prediction
Hebiri M, Lederer J. 2013 · 2013
Later among the works it cites.
A significance test for the lasso
Lockhart R, Taylor J, Tibshirani RRJ. 2013 · 2013
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On the Prediction Performance of the Lasso
Dalalyan AS, Hebiri M, Lederer J. 2014 · 2014
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