Regression Shrinkage and Selection via The Lasso: A Retrospective
Top Cited Papers
- 20 April 2011
- journal article
- Published by Oxford University Press (OUP) in Journal of the Royal Statistical Society Series B: Statistical Methodology
- Vol. 73 (3), 273-282
- https://doi.org/10.1111/j.1467-9868.2011.00771.x
Abstract
Summary: In the paper I give a brief review of the basic idea and some history and then discuss some developments since the original paper on regression shrinkage and selection via the lasso.This publication has 52 references indexed in Scilit:
- Joint Variable Selection for Fixed and Random Effects in Linear Mixed‐Effects ModelsBiometrics, 2010
- Stability SelectionJournal of the Royal Statistical Society Series B: Statistical Methodology, 2010
- Transposable regularized covariance models with an application to missing data imputationThe Annals of Applied Statistics, 2010
- Covariance-Regularized Regression and Classification for high Dimensional ProblemsJournal of the Royal Statistical Society Series B: Statistical Methodology, 2009
- One-step sparse estimates in nonconcave penalized likelihood modelsThe Annals of Statistics, 2008
- A coordinate gradient descent method for nonsmooth separable minimizationMathematical Programming, 2007
- Reversible jump Markov chain Monte Carlo computation and Bayesian model determinationBiometrika, 1995
- Better Subset Regression Using the Nonnegative GarroteTechnometrics, 1995
- Variable Selection via Gibbs SamplingJournal of the American Statistical Association, 1993
- A Statistical View of Some Chemometrics Regression ToolsTechnometrics, 1993