The following pages link to glmnet (Q20169):
Displaying 50 items.
- Solution paths for the generalized Lasso with applications to spatially varying coefficients regression (Q2008112) (← links)
- A scalable sparse Cholesky based approach for learning high-dimensional covariance matrices in ordered data (Q2008637) (← links)
- Local and global convergence of a general inertial proximal splitting scheme for minimizing composite functions (Q2013141) (← links)
- Greedy variance estimation for the LASSO (Q2019914) (← links)
- Learning causal effect using machine learning with application to China's typhoon (Q2023742) (← links)
- Equivalence between adaptive Lasso and generalized ridge estimators in linear regression with orthogonal explanatory variables after optimizing regularization parameters (Q2027229) (← links)
- Retail sales forecasting with meta-learning (Q2028846) (← links)
- Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains (Q2028883) (← links)
- A dual based semismooth Newton-type algorithm for solving large-scale sparse Tikhonov regularization problems (Q2033078) (← links)
- An extended Newton-type algorithm for \(\ell_2\)-regularized sparse logistic regression and its efficiency for classifying large-scale datasets (Q2033090) (← links)
- Necessary and sufficient conditions for variable selection consistency of the Lasso in high dimensions (Q2039788) (← links)
- High-dimensional variable selection via low-dimensional adaptive learning (Q2044323) (← links)
- Multicarving for high-dimensional post-selection inference (Q2044355) (← links)
- Iteratively reweighted \(\ell_1\)-penalized robust regression (Q2044416) (← links)
- Subspace quadratic regularization method for group sparse multinomial logistic regression (Q2044487) (← links)
- Eigenvector-based sparse canonical correlation analysis: fast computation for estimation of multiple canonical vectors (Q2048126) (← links)
- Imputation of clinical covariates in time series (Q2051236) (← links)
- From multivariate to functional data analysis: fundamentals, recent developments, and emerging areas (Q2062763) (← links)
- Learning social networks from text data using covariate information (Q2066718) (← links)
- AgFlow: fast model selection of penalized PCA via implicit regularization effects of gradient flow (Q2071349) (← links)
- Sparse classification: a scalable discrete optimization perspective (Q2071494) (← links)
- A survey of statistical learning techniques as applied to inexpensive pediatric obstructive sleep apnea data (Q2072600) (← links)
- Augmented minimax linear estimation (Q2073703) (← links)
- Tuning parameter calibration for personalized prediction in medicine (Q2074293) (← links)
- Broken adaptive ridge regression for right-censored survival data (Q2075449) (← links)
- Multiclass-penalized logistic regression (Q2076113) (← links)
- Dynamic large financial networks \textit{via} conditional expected shortfalls (Q2076940) (← links)
- Predicting competitions by combining conditional logistic regression and subjective Bayes: an Academy Awards case study (Q2078331) (← links)
- Quantile regression feature selection and estimation with grouped variables using Huber approximation (Q2080351) (← links)
- Achieving fairness with a simple ridge penalty (Q2080355) (← links)
- Scalar on network regression via boosting (Q2080790) (← links)
- Weighted thresholding homotopy method for sparsity constrained optimization (Q2082209) (← links)
- Weight smoothing for nonprobability surveys (Q2084713) (← links)
- Flexible, non-parametric modeling using regularized neural networks (Q2095735) (← links)
- Adaptive step-length selection in gradient boosting for Gaussian location and scale models (Q2095757) (← links)
- Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features (Q2095774) (← links)
- Sparse regression at scale: branch-and-bound rooted in first-order optimization (Q2097642) (← links)
- On regularization of generalized maximum entropy for linear models (Q2100166) (← links)
- Visualization and assessment of model selection uncertainty (Q2101381) (← links)
- Multivariate sparse Laplacian shrinkage for joint estimation of two graphical structures (Q2101407) (← links)
- Semi-parametric Bayes regression with network-valued covariates (Q2102416) (← links)
- A phase transition for finding needles in nonlinear haystacks with LASSO artificial neural networks (Q2103975) (← links)
- Deep learning architectures for nonlinear operator functions and nonlinear inverse problems (Q2113263) (← links)
- Regularization and variable selection in Heckman selection model (Q2122823) (← links)
- Sparse Laplacian shrinkage with the graphical Lasso estimator for regression problems (Q2125484) (← links)
- Estimation of dynamic systems using a method of characteristics filter (Q2125516) (← links)
- InfoGram and admissible machine learning (Q2127228) (← links)
- Sparse matrix linear models for structured high-throughput data (Q2135347) (← links)
- The variable selection by the Dantzig selector for Cox's proportional hazards model (Q2135519) (← links)
- A new double-regularized regression using Liu and Lasso regularization (Q2135849) (← links)
