The following pages link to glmnet (Q20169):
Displaying 50 items.
- A resampling approach for confidence intervals in linear time-series models after model selection (Q2683268) (← links)
- Double-estimation-friendly inference for high-dimensional misspecified models (Q2684689) (← links)
- Modeling cell populations measured by flow cytometry with covariates using sparse mixture of regressions (Q2686038) (← links)
- A primal and dual active set algorithm for truncated \(L_1\) regularized logistic regression (Q2691265) (← links)
- Robust projected principal component analysis for large-dimensional semiparametric factor modeling (Q2692929) (← links)
- Recovery of partly sparse and dense signals (Q2692936) (← links)
- Sparse estimation technique for digital pre-distortion of impedance-mismatched power amplifiers (Q2699680) (← links)
- Sparse estimation: an MMSE approach (Q2700880) (← links)
- A direct estimation of high dimensional stationary vector autoregressions (Q2788399) (← links)
- On semi-supervised linear regression in covariate shift problems (Q2788402) (← links)
- Shrinkage and penalty estimators of a Poisson regression model (Q2802803) (← links)
- Variable selection in linear mixed models using an extended class of penalties (Q2802814) (← links)
- Nonidentical twins: comparison of frequentist and Bayesian Lasso for Cox models (Q2803426) (← links)
- Penalized regression for interval-censored times of disease progression: selection of HLA markers in psoriatic arthritis (Q2803501) (← links)
- Local-aggregate modeling for big data via distributed optimization: applications to neuroimaging (Q2809515) (← links)
- A permutation approach for selecting the penalty parameter in penalized model selection (Q2809556) (← links)
- Compressed Gaussian process for manifold regression (Q2810879) (← links)
- On the characterization of a class of Fisher-consistent loss functions and its application to boosting (Q2810880) (← links)
- A predictive enrichment procedure to identify potential responders to a new therapy for randomized, comparative controlled clinical studies (Q2827203) (← links)
- A multilevel framework for sparse optimization with application to inverse covariance estimation and logistic regression (Q2830631) (← links)
- Bayesian model selection in logistic regression for the detection of adverse drug reactions (Q2833473) (← links)
- Variable selection using stepdown procedures in high-dimensional linear models (Q2833627) (← links)
- Multiple output regression with latent noise (Q2834436) (← links)
- Identification and efficient estimation of the natural direct effect among the untreated (Q2846436) (← links)
- Regression on manifolds using data-dependent regularization with applications in computer vision (Q2870760) (← links)
- Learning with many experts: model selection and sparsity (Q2870765) (← links)
- Applied Predictive Modeling (Q2874155) (← links)
- What subject matter questions motivate the use of machine learning approaches compared to statistical models for probability prediction? (Q2875749) (← links)
- Estimation of sparse binary pairwise Markov networks using pseudo-likelihoods (Q2880910) (← links)
- High-dimensional heteroscedastic regression with an application to eQTL data analysis (Q2894024) (← links)
- Estimation for high-dimensional linear mixed-effects models using \(\ell_1\)-penalization (Q2911662) (← links)
- Covariance-regularized regression and classification for high dimensional problems (Q2920259) (← links)
- New robust variable selection methods for linear regression models (Q2922164) (← links)
- Optimality of Graphlet Screening in High Dimensional Variable Selection (Q2934100) (← links)
- Laplace Error Penalty-based Variable Selection in High Dimension (Q2949868) (← links)
- (Q2953631) (← links)
- Non-asymptotic oracle inequalities for the Lasso and Group Lasso in high dimensional logistic model (Q2954238) (← links)
- Learning Oncogenic Pathways from Binary Genomic Instability Data (Q3008875) (← links)
- A Penalized Likelihood Approach for Bivariate Conditional Normal Models for Dynamic Co-expression Analysis (Q3008891) (← links)
- NESTA: A Fast and Accurate First-Order Method for Sparse Recovery (Q3077123) (← links)
- (Q3096176) (← links)
- A Bayesian Variable Selection Approach Yields Improved Detection of Brain Activation From Complex-Valued fMRI (Q3121154) (← links)
- Interpretable Dynamic Treatment Regimes (Q3121178) (← links)
- Sparse Pairwise Likelihood Estimation for Multivariate Longitudinal Mixed Models (Q3121567) (← links)
- Semi-automatic selection of summary statistics for ABC model choice (Q3191812) (← links)
- A direct approach to sparse discriminant analysis in ultra-high dimensions (Q3224213) (← links)
- Goodness-of-fit Tests for Functional Linear Models Based on Integrated Projections (Q3300634) (← links)
- A statistical mechanics approach to de-biasing and uncertainty estimation in LASSO for random measurements (Q3303301) (← links)
- Penalized and Constrained Optimization: An Application to High-Dimensional Website Advertising (Q3304839) (← links)
- PUlasso: High-Dimensional Variable Selection With Presence-Only Data (Q3304856) (← links)
