On Model Selection Consistency of Lasso

Keywords

Lasso (programming language), Model selection, Selection (genetic algorithm), Feature selection, Consistency (knowledge bases)

References

• Consistent neighbourhood selection for sparse high-dimensional graphs with the LassoNicolai Meinshausen, Peter Bühlmann — 2004
• A NOTE ON THE LASSO AND RELATED PROCEDURES IN MODEL SELECTIONChenlei Leng, Yi Lin, Grace Wahba — 2006
• Asymptotics for lasso-type estimatorsWenjiang Fu, Keith Knight — 2000
• On the LASSO and its DualM. R. Osborne, Brett Presnell, Berwin A. Turlach — 2000
• What Are Degrees of FreedomSanjay K. Pandey, C. L. Bright — 2008
• On the “degrees of freedom” of the lassoHui Zou, Trevor Hastie, Robert Tibshirani — 2007
• High-dimensional graphs and variable selection with the LassoNicolai Meinshausen, Peter Bühlmann — 2006
• Ridge Regression: Biased Estimation for Nonorthogonal ProblemsArthur E. Hoerl, Robert W. Kennard — 2000
• Stable recovery of sparse overcomplete representations in the presence of noiseDavid L. Donoho, Michael Elad, Vladimir Temlyakov — 2006
• Regression Shrinkage and Selection via the LassoRobert Tibshirani — 1996
• Boosted LassoPeng Zhao, Bin Yu — 2004
• Lasso with relaxationNicolai Meinshausen — 2005
• Least angle regressionBradley Efron, Trevor Hastie, Iain M. Johnstone — 2004
• Tracking Curved Regularized Optimization Solution PathsSaharon Rosset — 2004
• METHODOLOGIES IN SPECTRAL ANALYSIS OF LARGE DIMENSIONAL RANDOM MATRICES , A REVIEWZ. Bai — 1999
• High dimensional graphs and variable selection with the LassoNicolai Meinshausen, Peter Bühlmann — 2006

Cited by

• Sparse Signal Sampling using Noisy Linear ProjectionsGalen Reeves — 2008
• Local stability and robustness of sparse dictionary learning in the presence of noiseRodolphe Jenatton, Rémi Gribonval, Francis Bach — 2012
• Penalized Bregman divergence for large-dimensional regression and classification.弘 山本, 昭夫 角田, 寿治 西 — 2010
• A Sparse Structured Shrinkage Estimator for Nonparametric Varying-Coefficient Model with an Application in GenomicsJosé María Vázquez García-Peñuela — 2012
• Passive detection of radionuclides from weak and poorly resolved gamma-ray energy spectraPaul Kump — 2012
• AucPR: An AUC-based approach using penalized regression for disease prediction with high-dimensional omics dataAlberto Pérez de Muniain Ortigosa — 2014
• On the ℓ 1 -ℓ q Regularized RegressionHan Liu, Jian Zhang — 2008
• Data-driven methods for imputing national-level incidence in global burden of disease studiesДмитрий Анатольевич Буряк — 2015
• Grouped variable selection in high dimensional partially linear additive Cox modelLi Liu — 2010
• Variable Selection with Prior Information for Generalized Linear Models via the Prior LASSO MethodIrvin J. Farber — 2016
• A network-driven approach for genome-wide association mappingIka Kustiyah Oktaviyanti — 2016
• Variable Selection by Bayesian Adaptive Lasso and Iterative Adaptive Lasso, with Application for Genome-wide Multiple Loci MappingWei Sun, Joseph G. Ibrahim, Fei Zou — 2009
• A Unified Framework for Consistency of Regularized Loss MinimizersJean Honorio, Tommi Jaakkola — 2014
• Sparsity Based Efficient Cross-Correlation Techniques in Sensor NetworksPrasant Misra, Wen Hu, Mingrui Yang — 2015
• Deterministic Compressed SensingA.R. Calderbank, Robert E. Schapire, Sina Jafarpour — 2011
• Nonconvex selection in nonparametric additive modelsXiangmin Zhang, Dale Zimmerman, Jian Huang — 2014
• A Comparison of Optimization Methods and Software for Large-scale L1-regularized Linear ClassificationGuo-Xun Yuan, Kai-Wei Chang, Cho‐Jui Hsieh — 2010
• Confidence Intervals for Low-Dimensional Parameters With High-Dimensional DataCun‐Hui Zhang, Stephanie S. Zhang — 2011
• GROUP SPARSITY VIA LINEAR-TIME PROJECTIONE. van den Berg, Mark Schmidt, Michael P. Friedlander — 2008
• Sparse Group Selection Through Co-Adaptive PenaltiesZhou Fang — 2011

Related papers

• Statistics for High-Dimensional Data: Methods, Theory and ApplicationsPeter Bhlmann, Sara van de Geer — 2011
• Lasso-type recovery of sparse representations for high-dimensional dataMeinshausen, Nicolai, Yu, Bin — 2006
• Sparsity oracle inequalities for the LassoFlorentina Bunea, Alexandre B. Tsybakov, Marten Wegkamp — 2012
• High dimensional graphs and variable selection with the LassoNicolai Meinshausen, Peter Bühlmann — 2006
• Discussion of “Sure Independence Screening for Ultra-High Dimensional Feature SpaceJianqing Fan, Jinchi Lv — 2006
• Model selection and estimation in regression with grouped variablesMing Yuan, Yi Lin — 2006
• Regression Shrinkage and Selection via the LassoRobert Tibshirani — 1996