暂无分享,去创建一个
[2] Matthias Hein,et al. Non-negative least squares for high-dimensional linear models: consistency and sparse recovery without regularization , 2012, 1205.0953.
[3] Wenjiang J. Fu,et al. Asymptotics for lasso-type estimators , 2000 .
[4] Trevor J. Hastie,et al. The Graphical Lasso: New Insights and Alternatives , 2011, Electronic journal of statistics.
[5] N. Meinshausen. Sign-constrained least squares estimation for high-dimensional regression , 2012, 1202.0889.
[6] R. Tibshirani,et al. Covariance‐regularized regression and classification for high dimensional problems , 2009, Journal of the Royal Statistical Society. Series B, Statistical methodology.
[7] N. Meinshausen,et al. High-dimensional graphs and variable selection with the Lasso , 2006, math/0608017.
[8] H. Zou,et al. Regularization and variable selection via the elastic net , 2005 .
[9] José Crossa,et al. Prediction of Genetic Values of Quantitative Traits in Plant Breeding Using Pedigree and Molecular Markers , 2010, Genetics.
[10] S. Geer,et al. Correlated variables in regression: Clustering and sparse estimation , 2012, 1209.5908.
[11] Kam D. Dahlquist,et al. Regression Approaches for Microarray Data Analysis , 2002, J. Comput. Biol..
[12] J. Friedman,et al. New Insights and Faster Computations for the Graphical Lasso , 2011 .
[13] Y. Ritov,et al. Persistence in high-dimensional linear predictor selection and the virtue of overparametrization , 2004 .
[14] Michael A. Saunders,et al. Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..
[15] D. Donoho. For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solution , 2006 .
[16] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[17] Eytan Domany,et al. Classification of human astrocytic gliomas on the basis of gene expression: a correlated group of genes with angiogenic activity emerges as a strong predictor of subtypes. , 2003, Cancer research.
[18] Joel A. Tropp,et al. Greed is good: algorithmic results for sparse approximation , 2004, IEEE Transactions on Information Theory.
[19] Trevor Hastie,et al. Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.
[20] Ker-Chau Li,et al. A Model-Averaging Approach for High-Dimensional Regression , 2014 .
[21] Joel A. Tropp,et al. Just relax: convex programming methods for identifying sparse signals in noise , 2006, IEEE Transactions on Information Theory.
[22] Peng Zhao,et al. On Model Selection Consistency of Lasso , 2006, J. Mach. Learn. Res..
[23] R. Shafer,et al. Genotypic predictors of human immunodeficiency virus type 1 drug resistance , 2006, Proceedings of the National Academy of Sciences.
[24] S. Geer,et al. On the conditions used to prove oracle results for the Lasso , 2009, 0910.0722.
[25] Fionn Murtagh,et al. Clustering in massive data sets , 2002 .
[26] Trevor Hastie,et al. Averaged gene expressions for regression. , 2007, Biostatistics.
[27] A. Tsybakov,et al. Sparsity oracle inequalities for the Lasso , 2007, 0705.3308.
[28] Panos M. Pardalos,et al. Handbook of Massive Data Sets , 2002, Massive Computing.
[29] P. Fryzlewicz,et al. High dimensional variable selection via tilting , 2012, 1611.08640.
[30] Ali Shojaie,et al. The cluster graphical lasso for improved estimation of Gaussian graphical models , 2013, Comput. Stat. Data Anal..
[31] Robert Tibshirani,et al. Hybrid hierarchical clustering with applications to microarray data. , 2005, Biostatistics.