The following pages link to A note on adaptive group Lasso (Q1023903):
Displaying 50 items.
- Adaptive group Lasso selection in quantile models (Q2633421) (← links)
- Sparse regression with multi-type regularized feature modeling (Q2657005) (← links)
- Searching for minimal optimal neural networks (Q2667597) (← links)
- Adaptive bi-level variable selection for multivariate failure time model with a diverging number of covariates (Q2677126) (← links)
- Estimation of high-dimensional change-points under a group sparsity structure (Q2689607) (← links)
- Rank-based group variable selection (Q2832016) (← links)
- Sparse Convex Clustering (Q3391120) (← links)
- An improved Lasso method and its application in log-linear models (Q3461008) (← links)
- Sparse kernel machine regression for ordinal outcomes (Q3465724) (← links)
- Proximal gradient method with automatic selection of the parameter by automatic differentiation (Q4685565) (← links)
- Sparse Reduced-Rank Regression for Simultaneous Dimension Reduction and Variable Selection (Q4904730) (← links)
- Factor Selection and Structural Identification in the Interaction ANOVA Model (Q4919562) (← links)
- Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling (Q4975410) (← links)
- Adaptive elastic-net selection in a quantile model with diverging number of variable groups (Q4999858) (← links)
- Oracle Efficient Estimation of Structural Breaks in Cointegrating Regressions (Q5030952) (← links)
- Bayesian bridge regression (Q5035746) (← links)
- Modeling association between multivariate correlated outcomes and high-dimensional sparse covariates: the adaptive SVS method (Q5036571) (← links)
- The information detection for the generalized additive model (Q5036871) (← links)
- Data Integration with Oracle Use of External Information from Heterogeneous Populations (Q5057225) (← links)
- Variable selection with group LASSO approach: Application to Cox regression with frailty model (Q5082578) (← links)
- Structure identification and variable selection in geographically weighted regression models (Q5106911) (← links)
- Robust estimation and selection for single-index regression model (Q5107397) (← links)
- An algorithm for the multivariate group lasso with covariance estimation (Q5139028) (← links)
- Convex Optimization for Group Feature Selection in Networked Data (Q5139860) (← links)
- Adaptive sup-norm regularized simultaneous multiple quantiles regression (Q5169749) (← links)
- Bi-level variable selection via adaptive sparse group Lasso (Q5220909) (← links)
- Hierarchically penalized quantile regression (Q5222337) (← links)
- A Lasso-type Robust Variable Selection for Time-Course Microarray Data (Q5265839) (← links)
- Robust Signed-Rank Variable Selection in Linear Regression (Q5280257) (← links)
- An elastic-net penalized expectile regression with applications (Q5861466) (← links)
- A fast unified algorithm for solving group-lasso penalize learning problems (Q5963816) (← links)
- On the grouped selection and model complexity of the adaptive elastic net (Q5970617) (← links)
- Joint learning of multiple Granger causal networks via non-convex regularizations: inference of group-level brain connectivity (Q6072512) (← links)
- Automatic selection by penalized asymmetric <i> L <sub>q</sub> </i> -norm in a high-dimensional model with grouped variables (Q6083206) (← links)
- Modelling Clustered Heterogeneity: Fixed Effects, Random Effects and Mixtures (Q6086487) (← links)
- Penetrating sporadic return predictability (Q6090551) (← links)
- Individual Data Protected Integrative Regression Analysis of High-Dimensional Heterogeneous Data (Q6110723) (← links)
- Robust variable selection in semiparametric mixed effects longitudinal data models (Q6118231) (← links)
- Bayesian weighted composite quantile regression estimation for linear regression models with autoregressive errors (Q6541121) (← links)
- Partially constrained group variable selection to adjust for complementary unit performance in American college football (Q6547163) (← links)
- Simultaneous estimation and variable selection for a non-crossing multiple quantile regression using deep neural networks (Q6547780) (← links)
- Reducing bias and mitigating the influence of excess of zeros in regression covariates with multi-outcome adaptive LAD-lasso (Q6573032) (← links)
- Active-set based block coordinate descent algorithm in group LASSO for self-exciting threshold autoregressive model (Q6581310) (← links)
- Response variable selection in multivariate linear regression (Q6593365) (← links)
- A three-stage approach to identify biomarker signatures for cancer genetic data with survival endpoints (Q6596729) (← links)
- Oracle inequalities for weighted group Lasso in high-dimensional Poisson regression model (Q6597414) (← links)
- Model selection by pathwise marginal likelihood thresholding (Q6606021) (← links)
- A hierarchical integrative group least absolute shrinkage and selection operator for analyzing environmental mixtures (Q6617835) (← links)
- Doubly structured sparsity for grouped multivariate responses with application to functional outcome score modeling (Q6625800) (← links)
- Bayesian adaptive group Lasso with semiparametric hidden Markov models (Q6625986) (← links)
