The following pages link to glmnet (Q20169):
Displaying 50 items.
- On Hodges' superefficiency and merits of oracle property in model selection (Q2330527) (← links)
- Shrinkage priors for Bayesian penalized regression (Q2332812) (← links)
- High-dimensional Ising model selection with Bayesian information criteria (Q2340871) (← links)
- Improving the efficiency of genomic selection (Q2344249) (← links)
- Variable selection and estimation for semi-parametric multiple-index models (Q2345120) (← links)
- Preconditioning the Lasso for sign consistency (Q2346526) (← links)
- A penalty approach to differential item functioning in Rasch models (Q2348182) (← links)
- A new test for part of high dimensional regression coefficients (Q2348453) (← links)
- Coordinate descent algorithms (Q2349114) (← links)
- Continuous-time discrete-space models for animal movement (Q2349561) (← links)
- Innovated interaction screening for high-dimensional nonlinear classification (Q2352740) (← links)
- Regression model based on convex combinations best correlated with response (Q2354471) (← links)
- Sparse regularized discriminant analysis with application to microarrays (Q2359339) (← links)
- Interpretable dimension reduction for classifying functional data (Q2359481) (← links)
- An extended variable inclusion and shrinkage algorithm for correlated variables (Q2359516) (← links)
- Structured regularization for conditional Gaussian graphical models (Q2361457) (← links)
- Penalized variable selection in competing risks regression (Q2364037) (← links)
- Latent variable selection for multidimensional item response theory models via \(L_{1}\) regularization (Q2364845) (← links)
- Performance of first- and second-order methods for \(\ell_1\)-regularized least squares problems (Q2374363) (← links)
- A statistical framework for pathway and gene identification from integrative analysis (Q2400810) (← links)
- Dimension-reduced clustering of functional data via subspace separation (Q2403303) (← links)
- IPF-LASSO: integrative \(L_1\)-penalized regression with penalty factors for prediction based on multi-omics data (Q2405418) (← links)
- Regularized estimation in sparse high-dimensional multivariate regression, with application to a DNA methylation study (Q2406186) (← links)
- Leading impulse response identification via the elastic net criterion (Q2407167) (← links)
- Variable selection through adaptive MAVE (Q2407490) (← links)
- Variable selection for partially linear models via learning gradients (Q2408225) (← links)
- Structure learning of sparse directed acyclic graphs incorporating the scale-free property (Q2418068) (← links)
- Convolutional neural network models of V1 responses to complex patterns (Q2418231) (← links)
- Quantile regression with group Lasso for classification (Q2418274) (← links)
- A uniform framework for the combination of penalties in generalized structured models (Q2418291) (← links)
- Accelerating block coordinate descent methods with identification strategies (Q2419524) (← links)
- Arbitrage of forecasting experts (Q2425238) (← links)
- Shrinkage estimation of partially linear single-index models (Q2435757) (← links)
- Ultrahigh dimensional variable selection through the penalized maximum trimmed likelihood estimator (Q2442684) (← links)
- Adaptive Lasso estimators for ultrahigh dimensional generalized linear models (Q2453901) (← links)
- Multicategory large margin classification methods: hinge losses vs. coherence functions (Q2510115) (← links)
- Strong oracle optimality of folded concave penalized estimation (Q2510819) (← links)
- A note on the one-step estimator for ultrahigh dimensionality (Q2511184) (← links)
- Maximin effects in inhomogeneous large-scale data (Q2515497) (← links)
- A parallel line search subspace correction method for composite convex optimization (Q2516372) (← links)
- Stability of feature selection in classification issues for high-dimensional correlated data (Q2628882) (← links)
- Maximizing interpretability and cost-effectiveness of surgical site infection (SSI) predictive models using feature-specific regularized logistic regression on preoperative temporal data (Q2632251) (← links)
- A weight function method for selection of proteins to predict an outcome using protein expression data (Q2656115) (← links)
- A machine learning efficient frontier (Q2661535) (← links)
- Graph structured sparse subset selection (Q2662712) (← links)
- On sparse optimal regression trees (Q2670540) (← links)
- Smooth LASSO estimator for the function-on-function linear regression model (Q2674500) (← links)
- Predictive modeling in a steelmaking process using optimized relevance vector regression and support vector regression (Q2675703) (← links)
- Loss amount prediction from textual data using a double GLM with shrinkage and selection (Q2677931) (← links)
- Nearly optimal Bayesian shrinkage for high-dimensional regression (Q2683046) (← links)
