Pages that link to "Item:Q2500458"

The following pages link to High-dimensional graphs and variable selection with the Lasso (Q2500458):

Displaying 50 items.

• Inferring large graphs using \(\ell_1\)-penalized likelihood (Q1704026) (← links)
• Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity (Q1704910) (← links)
• Asymptotically honest confidence regions for high dimensional parameters by the desparsified conservative Lasso (Q1706454) (← links)
• Heterogeneity adjustment with applications to graphical model inference (Q1711558) (← links)
• Spectral clustering via sparse graph structure learning with application to proteomic signaling networks in cancer (Q1727851) (← links)
• Combinatorial inference for graphical models (Q1731056) (← links)
• Efficient Bayesian regularization for graphical model selection (Q1738143) (← links)
• Robust covariance estimation for approximate factor models (Q1739628) (← links)
• Stable limit theorems for empirical processes under conditional neighborhood dependence (Q1740523) (← links)
• Learning semidefinite regularizers (Q1740575) (← links)
• Pathwise coordinate optimization for sparse learning: algorithm and theory (Q1747736) (← links)
• Regularization and the small-ball method. I: Sparse recovery (Q1750281) (← links)
• Gaussian and bootstrap approximations for high-dimensional U-statistics and their applications (Q1750282) (← links)
• Inference of the stochastic MAPK pathway by modified diffusion bridge method (Q1788913) (← links)
• High-dimensional inference: confidence intervals, \(p\)-values and R-software \texttt{hdi} (Q1790302) (← links)
• Broken adaptive ridge regression and its asymptotic properties (Q1795597) (← links)
• An efficient algorithm for sparse inverse covariance matrix estimation based on dual formulation (Q1796959) (← links)
• Stable graphical model estimation with random forests for discrete, continuous, and mixed variables (Q1800084) (← links)
• Variable selection with Hamming loss (Q1800786) (← links)
• Semiparametric efficiency bounds for high-dimensional models (Q1800804) (← links)
• Fitting very large sparse Gaussian graphical models (Q1927038) (← links)
• \(\ell _{1}\)-regularized linear regression: persistence and oracle inequalities (Q1930861) (← links)
• Model selection via standard error adjusted adaptive Lasso (Q1934485) (← links)
• Simultaneous variable selection and estimation in semiparametric modeling of longitudinal/clustered data (Q1940758) (← links)
• Discussion: Latent variable graphical model selection via convex optimization (Q1940763) (← links)
• Rejoinder: Latent variable graphical model selection via convex optimization (Q1940764) (← links)
• High-dimensional semiparametric Gaussian copula graphical models (Q1940774) (← links)
• Structural learning for Bayesian networks by testing complete separators in prime blocks (Q1942894) (← links)
• Minimax risks for sparse regressions: ultra-high dimensional phenomenons (Q1950804) (← links)
• Group symmetry and covariance regularization (Q1950873) (← links)
• Estimating networks with jumps (Q1950892) (← links)
• The graphical lasso: new insights and alternatives (Q1950894) (← links)
• Bootstrap inference for network construction with an application to a breast cancer microarray study (Q1951540) (← links)
• Sparse permutation invariant covariance estimation (Q1951760) (← links)
• Estimation of Gaussian graphs by model selection (Q1951762) (← links)
• On the asymptotic properties of the group lasso estimator for linear models (Q1951765) (← links)
• Honest variable selection in linear and logistic regression models via \(\ell _{1}\) and \(\ell _{1}+\ell _{2}\) penalization (Q1951794) (← links)
• Inferring sparse Gaussian graphical models with latent structure (Q1951974) (← links)
• High dimensional sparse covariance estimation via directed acyclic graphs (Q1952020) (← links)
• Forest Garrote (Q1952025) (← links)
• On the conditions used to prove oracle results for the Lasso (Q1952029) (← links)
• Self-concordant analysis for logistic regression (Q1952060) (← links)
• PAC-Bayesian bounds for sparse regression estimation with exponential weights (Q1952177) (← links)
• The adaptive and the thresholded Lasso for potentially misspecified models (and a lower bound for the Lasso) (Q1952206) (← links)
• High-dimensional covariance estimation by minimizing \(\ell _{1}\)-penalized log-determinant divergence (Q1952214) (← links)
• Robust regression through the Huber's criterion and adaptive lasso penalty (Q1952217) (← links)
• The smooth-Lasso and other \(\ell _{1}+\ell _{2}\)-penalized methods (Q1952223) (← links)
• Time varying undirected graphs (Q1959601) (← links)
• ROCKET: robust confidence intervals via Kendall's tau for transelliptical graphical models (Q1990586) (← links)
• Debiasing the Lasso: optimal sample size for Gaussian designs (Q1991670) (← links)