Pages that link to "Item:Q3631444"

The following pages link to The Group Lasso for Logistic Regression (Q3631444):

Displaying 45 items.

• Local optimality for stationary points of group zero-norm regularized problems and equivalent surrogates (Q6051201) (← links)
• Generalized linear models with structured sparsity estimators (Q6054394) (← links)
• Two-stage penalized algorithms via integrating prior information improve gene selection from omics data (Q6054652) (← links)
• Group sparse recovery via group square-root elastic net and the iterative multivariate thresholding-based algorithm (Q6065672) (← links)
• An Efficient Convex Formulation for Reduced-Rank Linear Discriminant Analysis in High Dimensions (Q6069866) (← links)
• Model-Based Clustering of High-Dimensional Longitudinal Data via Regularization (Q6079763) (← links)
• A unified analysis of convex and non-convex \(\ell_p\)-ball projection problems (Q6097485) (← links)
• Linearly-convergent FISTA variant for composite optimization with duality (Q6101606) (← links)
• Introducing Lasso-type penalisation to generalised joint regression modelling for count data (Q6107410) (← links)
• Statistical Inference for High-Dimensional Generalized Linear Models With Binary Outcomes (Q6110021) (← links)
• A Review of Data‐Driven Discovery for Dynamic Systems (Q6131430) (← links)
• A Bayesian group selection with compositional responses for analysis of radiologic tumor proportions and their genomic determinants (Q6138618) (← links)
• A convex-Nonconvex strategy for grouped variable selection (Q6144411) (← links)
• Lasso in Infinite dimension: application to variable selection in functional multivariate linear regression (Q6144429) (← links)
• Robust estimation in regression and classification methods for large dimensional data (Q6176235) (← links)
• A backpropagation learning algorithm with graph regularization for feedforward neural networks (Q6195182) (← links)
• Variable selection in generalized functional linear models (Q6537841) (← links)
• Moving force identification based on group Lasso and compressed sensing (Q6538036) (← links)
• A nonparametric method for classification trees using grouped covariates (Q6550298) (← links)
• Nonparametric augmented probability weighting with sparsity (Q6554241) (← links)
• A fast trans-lasso algorithm with penalized weighted score function (Q6554251) (← links)
• Estimation of \(l_0\) norm penalized models: a statistical treatment (Q6554254) (← links)
• A feature selection method for classification based on ensemble of penalized logistic models (Q6558498) (← links)
• A generalized formulation for group selection via ADMM (Q6571367) (← links)
• Statistical learning for variable annuity policyholder withdrawal behavior (Q6574700) (← links)
• Variable selection for generalized linear model with highly correlated covariates (Q6580088) (← links)
• Variable selection using P-splines (Q6604438) (← links)
• Grouped feature screening for ultrahigh-dimensional classification via Gini distance correlation (Q6615379) (← links)
• Variable selection in binary logistic regression for modelling bankruptcy risk (Q6615795) (← links)
• Robust integrative analysis via quantile regression with homogeneity and sparsity (Q6616189) (← links)
• An Interactive Greedy Approach to Group Sparsity in High Dimensions (Q6621653) (← links)
• Maximum diversity weighting for biomarkers with application in HIV-1 vaccine studies (Q6625189) (← links)
• Regularized parametric survival modeling to improve risk prediction models (Q6625393) (← links)
• Functional multivariable logistic regression with an application to HIV viral suppression prediction (Q6625485) (← links)
• Structured sparse logistic regression with application to lung cancer prediction using breath volatile biomarkers (Q6627506) (← links)
• A classification for complex imbalanced data in disease screening and early diagnosis (Q6628493) (← links)
• Variable selection for discrete survival model with frailty in presence of left truncation and right censoring: studying association of environmental toxicants on time-to-pregnancy (Q6629922) (← links)
• Boundedness and convergence analysis of a pi-sigma neural network based on online gradient method and sparse optimization (Q6630932) (← links)
• Bayesian Inference Using the Proximal Mapping: Uncertainty Quantification Under Varying Dimensionality (Q6631685) (← links)
• DDAC-SpAM: A Distributed Algorithm for Fitting High-dimensional Sparse Additive Models with Feature Division and Decorrelation (Q6631694) (← links)
• Bayesian variable selection methods for matched case-control studies (Q6636131) (← links)
• Bayesian approaches to variable selection: a comparative study from practical perspectives (Q6637079) (← links)
• GKF-PUAL: a group kernel-free approach to positive-unlabeled learning with variable selection (Q6645063) (← links)
• A flexible adaptive lasso Cox frailty model based on the full likelihood (Q6649351) (← links)
• A novel block-coordinate gradient descent algorithm for simultaneous grouped selection of fixed and random effects in joint modeling (Q6652620) (← links)