The following pages link to glmnet (Q20169):
Displaying 50 items.
- Differential Markov random field analysis with an application to detecting differential microbial community networks (Q4973623) (← links)
- Detection of spatially sparse damage using impulse response sensitivity and LASSO regularization (Q4988507) (← links)
- (Q4999036) (← links)
- Bayesian Regression With Undirected Network Predictors With an Application to Brain Connectome Data (Q4999134) (← links)
- (Q5004047) (← links)
- A non-convex regularization approach for stable estimation of loss development factors (Q5014498) (← links)
- Propensity score prediction for electronic healthcare databases using super learner and high-dimensional propensity score methods (Q5034151) (← links)
- Investigating competition in financial markets: a sparse autologistic model for dynamic network data (Q5035721) (← links)
- Bayesian bridge regression (Q5035746) (← links)
- A novel Bayesian regression model for counts with an application to health data (Q5035755) (← links)
- Sure independence screening for real medical Poisson data (Q5036508) (← links)
- Robust sparse regression by modeling noise as a mixture of gaussians (Q5036623) (← links)
- An empirical threshold of selection probability for analysis of high-dimensional correlated data (Q5036882) (← links)
- A Cholesky-based estimation for large-dimensional covariance matrices (Q5037036) (← links)
- Stability enhanced variable selection for a semiparametric model with flexible missingness mechanism and its application to the ChAMP study (Q5037060) (← links)
- REMI: REGRESSION WITH MARGINAL INFORMATION AND ITS APPLICATION IN GENOME-WIDE ASSOCIATION STUDIES (Q5037800) (← links)
- Prediction intervals for GLMs, GAMs, and some survival regression models (Q5039828) (← links)
- Finite-sample results for lasso and stepwise Neyman-orthogonal Poisson estimators (Q5040541) (← links)
- Poisson Regression With Error Corrupted High Dimensional Features (Q5041344) (← links)
- Comparative study of <i>L</i><sub>1</sub> regularized logistic regression methods for variable selection (Q5042097) (← links)
- Variance-estimation-free test of significant covariates in high-dimensional regression (Q5042192) (← links)
- Semiparametric Regression for Dual Population Mortality (Q5043477) (← links)
- Robust low-rank tensor factorization by cyclic weighted median (Q5046469) (← links)
- A maximum entropy copula model for mixed data: representation, estimation and applications (Q5051339) (← links)
- An efficient approach for discriminant analysis based on adaptive feature augmentation (Q5055246) (← links)
- An expectation maximization algorithm for high-dimensional model selection for the Ising model with misclassified states* (Q5056934) (← links)
- A Problem of Distributive Justice, Solved by the Lasso (Q5056960) (← links)
- Linear Aggregation in Tree-Based Estimators (Q5057103) (← links)
- GEE-Assisted Forward Regression for Spatial Latent Variable Models (Q5057226) (← links)
- Variable Selection with Multiply-Imputed Datasets: Choosing Between Stacked and Grouped Methods (Q5057238) (← links)
- A Scalable Hierarchical Lasso for Gene–Environment Interactions (Q5057242) (← links)
- Efficient Learning of Quadratic Variance Function Directed Acyclic Graphs via Topological Layers (Q5057262) (← links)
- Dermoscopic Image Classification with Neural Style Transfer (Q5057269) (← links)
- Features Selection as a Nash-Bargaining Solution: Applications in Online Advertising and Information Systems (Q5057996) (← links)
- Analysis of overfitting in the regularized Cox model (Q5059054) (← links)
- Cross validation in sparse linear regression with piecewise continuous nonconvex penalties and its acceleration (Q5059117) (← links)
- Hard Thresholding Regularised Logistic Regression: Theory and Algorithms (Q5061727) (← links)
- Bayesian bootstrap adaptive lasso estimators of regression models (Q5065281) (← links)
- A Pliable Lasso (Q5065986) (← links)
- Graph-Assisted Inverse Regression for Count Data and Its Application to Sequencing Data (Q5065992) (← links)
- A Scalable Empirical Bayes Approach to Variable Selection in Generalized Linear Models (Q5066001) (← links)
- Assessing and Visualizing Simultaneous Simulation Error (Q5066389) (← links)
- Local Linear Forests (Q5066400) (← links)
- Nonlinear Variable Selection via Deep Neural Networks (Q5066407) (← links)
- Spectrally Sparse Nonparametric Regression via Elastic Net Regularized Smoothers (Q5066429) (← links)
- Trace Ratio Optimization for High-Dimensional Multi-Class Discrimination (Q5066430) (← links)
- Model Selection With Lasso-Zero: Adding Straw to the Haystack to Better Find Needles (Q5066436) (← links)
- MIP-BOOST: Efficient and Effective <i>L</i><sub>0</sub> Feature Selection for Linear Regression (Q5066443) (← links)
- Scalable Algorithms for Large Competing Risks Data (Q5066454) (← links)
- Hierarchical Total Variations and Doubly Penalized ANOVA Modeling for Multivariate Nonparametric Regression (Q5066471) (← links)
