Pages that link to "Item:Q2500458"

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

Displaying 50 items.

• On constrained and regularized high-dimensional regression (Q380022) (← links)
• Asymptotic properties of Lasso+mLS and Lasso+Ridge in sparse high-dimensional linear regression (Q389956) (← links)
• Adjusting for high-dimensional covariates in sparse precision matrix estimation by \(\ell_1\)-penalization (Q391559) (← links)
• Non-negative least squares for high-dimensional linear models: consistency and sparse recovery without regularization (Q391843) (← links)
• Variable selection in high-dimensional quantile varying coefficient models (Q391871) (← links)
• Correlated variables in regression: clustering and sparse estimation (Q394080) (← links)
• High-dimensional covariance matrix estimation with missing observations (Q395991) (← links)
• Spatio-temporal random fields: compressible representation and distributed estimation (Q399911) (← links)
• Berry-Esseen bounds for estimating undirected graphs (Q405339) (← links)
• Classifier variability: accounting for training and testing (Q411930) (← links)
• Model selection and estimation in the matrix normal graphical model (Q413758) (← links)
• Profiled adaptive elastic-net procedure for partially linear models with high-dimensional covar\-i\-ates (Q419271) (← links)
• Group selection in high-dimensional partially linear additive models (Q424816) (← links)
• Adaptive cluster expansion for the inverse Ising problem: convergence, algorithm and tests (Q425214) (← links)
• Sharp support recovery from noisy random measurements by \(\ell_1\)-minimization (Q427066) (← links)
• Coordinate ascent for penalized semiparametric regression on high-dimensional panel count data (Q429611) (← links)
• Semiparametric regression models with additive nonparametric components and high dimensional parametric components (Q435000) (← links)
• Modeling dependent gene expression (Q439142) (← links)
• Sparse regression learning by aggregation and Langevin Monte-Carlo (Q439987) (← links)
• Mirror averaging with sparsity priors (Q442083) (← links)
• The log-linear group-lasso estimator and its asymptotic properties (Q442085) (← links)
• Simultaneous multiple response regression and inverse covariance matrix estimation via penalized Gaussian maximum likelihood (Q444979) (← links)
• Transductive versions of the Lasso and the Dantzig selector (Q447611) (← links)
• Estimation in high-dimensional linear models with deterministic design matrices (Q447831) (← links)
• General nonexact oracle inequalities for classes with a subexponential envelope (Q447832) (← links)
• Multiple testing and error control in Gaussian graphical model selection (Q449776) (← links)
• Boosting algorithms: regularization, prediction and model fitting (Q449780) (← links)
• Covariance estimation: the GLM and regularization perspectives (Q449843) (← links)
• Regularization for Cox's proportional hazards model with NP-dimensionality (Q449987) (← links)
• UPS delivers optimal phase diagram in high-dimensional variable selection (Q450021) (← links)
• Learning high-dimensional directed acyclic graphs with latent and selection variables (Q450035) (← links)
• Quadratic approximation on SCAD penalized estimation (Q452598) (← links)
• Focused vector information criterion model selection and model averaging regression with missing response (Q464389) (← links)
• Bayesian high-dimensional screening via MCMC (Q466528) (← links)
• Posterior convergence rates for estimating large precision matrices using graphical models (Q470497) (← links)
• Estimation of high-dimensional partially-observed discrete Markov random fields (Q470504) (← links)
• Covariate assisted screening and estimation (Q482879) (← links)
• A new perspective on least squares under convex constraint (Q482891) (← links)
• CAM: causal additive models, high-dimensional order search and penalized regression (Q482906) (← links)
• Bayesian sparse graphical models for classification with application to protein expression data (Q484003) (← links)
• High-dimensional Bayesian inference in nonparametric additive models (Q485930) (← links)
• Operator-valued kernel-based vector autoregressive models for network inference (Q493746) (← links)
• On the residual empirical process based on the ALASSO in high dimensions and its functional oracle property (Q494167) (← links)
• Oracle inequalities for high dimensional vector autoregressions (Q494169) (← links)
• On nonparametric feature filters in electromagnetic imaging (Q499441) (← links)
• Individual-specific, sparse inverse covariance estimation in generalized estimating equations (Q504468) (← links)
• Accelerating a Gibbs sampler for variable selection on genomics data with summarization and variable pre-selection combining an array DBMS and R (Q506436) (← links)
• Bayesian graphical models for differential pathways (Q516442) (← links)
• Learning Gaussian graphical models with fractional marginal pseudo-likelihood (Q518603) (← links)
• Shrinkage tuning parameter selection in precision matrices estimation (Q538141) (← links)