The following pages link to glmnet (Q20169):
Displaying 50 items.
- GSDAR: a fast Newton algorithm for \(\ell_0\) regularized generalized linear models with statistical guarantee (Q2135875) (← links)
- Evaluating class and school effects on the joint student achievements in different subjects: a bivariate semiparametric model with random coefficients (Q2135906) (← links)
- A general framework for tensor screening through smoothing (Q2136613) (← links)
- Aggregated hold out for sparse linear regression with a robust loss function (Q2136632) (← links)
- Penalized estimation of threshold auto-regressive models with many components and thresholds (Q2136665) (← links)
- An accelerated coordinate gradient descent algorithm for non-separable composite optimization (Q2139254) (← links)
- Fractional Tikhonov regularization to improve the performance of extreme learning machines (Q2141124) (← links)
- High-dimensional regression with potential prior information on variable importance (Q2152561) (← links)
- Sparse latent factor regression models for genome-wide and epigenome-wide association studies (Q2162483) (← links)
- The backbone method for ultra-high dimensional sparse machine learning (Q2163249) (← links)
- Central subspaces review: methods and applications (Q2172454) (← links)
- Sparse high-dimensional regression: exact scalable algorithms and phase transitions (Q2176621) (← links)
- Differential network inference via the fused D-trace loss with cross variables (Q2180062) (← links)
- A fast and consistent variable selection method for high-dimensional multivariate linear regression with a large number of explanatory variables (Q2180065) (← links)
- Separating variables to accelerate non-convex regularized optimization (Q2181546) (← links)
- Hierarchical inference for genome-wide association studies: a view on methodology with software (Q2184390) (← links)
- Projective inference in high-dimensional problems: prediction and feature selection (Q2188473) (← links)
- Primal path algorithm for compositional data analysis (Q2189589) (← links)
- Inference for high-dimensional instrumental variables regression (Q2190211) (← links)
- Dynamic tail inference with log-Laplace volatility (Q2191426) (← links)
- Best subset selection via cross-validation criterion (Q2192029) (← links)
- Bayesian variable selection for survival data using inverse moment priors (Q2194467) (← links)
- Synchronous parallel block coordinate descent method for nonsmooth convex function minimization (Q2200102) (← links)
- A safe reinforced feature screening strategy for Lasso based on feasible solutions (Q2201661) (← links)
- Variable selection for sparse logistic regression (Q2202033) (← links)
- Sparse directed acyclic graphs incorporating the covariates (Q2208417) (← links)
- When and when not to use optimal model averaging (Q2208423) (← links)
- The conditional censored graphical Lasso estimator (Q2209704) (← links)
- Generalised joint regression for count data: a penalty extension for competitive settings (Q2209714) (← links)
- On the choice of high-dimensional regression parameters in Gaussian random tomography (Q2211069) (← links)
- Interpretable regularized class association rules algorithm for classification in a categorical data space (Q2212562) (← links)
- Evaluating time series forecasting models: an empirical study on performance estimation methods (Q2217396) (← links)
- Worst-case complexity of cyclic coordinate descent: \(O(n^2)\) gap with randomized version (Q2220668) (← links)
- Sparse regression: scalable algorithms and empirical performance (Q2225311) (← links)
- Best subset, forward stepwise or Lasso? Analysis and recommendations based on extensive comparisons (Q2225312) (← links)
- A look at robustness and stability of \(\ell_1\)-versus \(\ell_0\)-regularization: discussion of papers by Bertsimas et al. and Hastie et al. (Q2225318) (← links)
- Model-based feature selection and clustering of RNA-seq data for unsupervised subtype discovery (Q2233190) (← links)
- Improved outcome prediction across data sources through robust parameter tuning (Q2236766) (← links)
- The value of text for small business default prediction: a deep learning approach (Q2239924) (← links)
- How can lenders prosper? Comparing machine learning approaches to identify profitable peer-to-peer loan investments (Q2240005) (← links)
- A clustering-based feature selection method for automatically generated relational attributes (Q2241179) (← links)
- Variable selection in the Box-Cox power transformation model (Q2242871) (← links)
- A statistical pipeline for identifying physical features that differentiate classes of 3D shapes (Q2245140) (← links)
- Scalable penalized spatiotemporal land-use regression for ground-level nitrogen dioxide (Q2245146) (← links)
- Data-driven RANS closures for three-dimensional flows around bluff bodies (Q2245405) (← links)
- Qini-based uplift regression (Q2247471) (← links)
- Orthogonal subsampling for big data linear regression (Q2247473) (← links)
- Estrogen receptor expression on breast cancer patients' survival under shape-restricted Cox regression model (Q2247474) (← links)
- A significance test for the lasso (Q2249837) (← links)
- Discussion: ``A significance test for the lasso'' (Q2249838) (← links)
