Pages that link to "Item:Q3631444"

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

Displaying 50 items.

• Exact penalties for decomposable convex optimization problems (Q5058390) (← links)
• MIP-BOOST: Efficient and Effective <i>L</i>₀ Feature Selection for Linear Regression (Q5066443) (← links)
• Robust variable selection in the logistic regression model (Q5071994) (← links)
• Overlapping group lasso for high-dimensional generalized linear models (Q5076945) (← links)
• Variable selection with group LASSO approach: Application to Cox regression with frailty model (Q5082578) (← links)
• Ensemble of penalized logistic models for classification of high-dimensional data (Q5082679) (← links)
• A modified multinomial baseline logit model with logit functions having different covariates (Q5083903) (← links)
• Grouped Transformations and Regularization in High-Dimensional Explainable ANOVA Approximation (Q5084523) (← links)
• A statistical methodology to select covariates in high-dimensional data under dependence. Application to the classification of genetic profiles in oncology (Q5085654) (← links)
• A new Gibbs sampler for Bayesian lasso (Q5086323) (← links)
• Simultaneous variable and factor selection via sparse group lasso in factor analysis (Q5107489) (← links)
• A consistent variable screening procedure with family-wise error control (Q5107774) (← links)
• Prediction in Cancer Genomics Using Topological Signatures and Machine Learning (Q5118370) (← links)
• On the advantages of the non-concave penalized likelihood model selection method with minimum prediction errors in large-scale medical studies (Q5123493) (← links)
• Grouped penalization estimation of the osteoporosis data in the traditional Chinese medicine (Q5128952) (← links)
• Construction of disease risk scoring systems using logistic group lasso: application to porcine reproductive and respiratory syndrome survey data (Q5128957) (← links)
• Multi-Armed Angle-Based Direct Learning for Estimating Optimal Individualized Treatment Rules With Various Outcomes (Q5130612) (← links)
• Time-varying Hazards Model for Incorporating Irregularly Measured, High-Dimensional Biomarkers (Q5134494) (← links)
• A new multicollinearity diagnostic for generalized linear models (Q5138141) (← links)
• A link-free sparse group variable selection method for single-index model (Q5138715) (← links)
• An algorithm for the multivariate group lasso with covariance estimation (Q5139028) (← links)
• A generalized additive model approach to time-to-event analysis (Q5142210) (← links)
• (Q5149262) (← links)
• Iteratively Reweighted Group Lasso Based on Log-Composite Regularization (Q5161764) (← links)
• Computation of second-order directional stationary points for group sparse optimization (Q5210743) (← links)
• (Q5214207) (← links)
• Model selection and parameter estimation of a multinomial logistic regression model (Q5219998) (← links)
• Regularized receiver operating characteristic-based logistic regression for grouped variable selection with composite criterion (Q5220892) (← links)
• Bi-level variable selection via adaptive sparse group Lasso (Q5220909) (← links)
• Causal Interaction in Factorial Experiments: Application to Conjoint Analysis (Q5231479) (← links)
• <b><tt>cmenet</tt></b>: A New Method for Bi-Level Variable Selection of Conditional Main Effects (Q5231511) (← links)
• Structural identification and variable selection in high-dimensional varying-coefficient models (Q5266564) (← links)
• Volumes of unit balls of mixed sequence spaces (Q5275855) (← links)
• The pre-image problem for Laplacian Eigenmaps utilizing <i>L</i> ₁ regularization with applications to data fusion (Q5348008) (← links)
• High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso (Q5378315) (← links)
• A differential-geometric approach to generalized linear models with grouped predictors (Q5384395) (← links)
• A Logistic Normal Multinomial Regression Model for Microbiome Compositional Data Analysis (Q5408029) (← links)
• Iteration Complexity of a Block Coordinate Gradient Descent Method for Convex Optimization (Q5501228) (← links)
• Variance estimation based on blocked 3×2 cross-validation in high-dimensional linear regression (Q5861470) (← links)
• Taylor quasi-likelihood for limited generalized linear models (Q5861540) (← links)
• Bayesian penalized model for classification and selection of functional predictors using longitudinal MRI data from ADNI (Q5880142) (← links)
• Model averaging for generalized linear models in fragmentary data prediction (Q5880143) (← links)
• A Trust-region Method for Nonsmooth Nonconvex Optimization (Q5881403) (← links)
• (Q5886010) (← links)
• Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors (Q5962732) (← links)
• A fast unified algorithm for solving group-lasso penalize learning problems (Q5963816) (← links)
• A selective review of group selection in high-dimensional models (Q5965305) (← links)
• Solving constrained nonsmooth group sparse optimization via group Capped-\(\ell_1\) relaxation and group smoothing proximal gradient algorithm (Q6043130) (← links)
• Covariance prediction via convex optimization (Q6050386) (← links)
• Bayesian group selection in logistic regression with application to MRI data analysis (Q6050939) (← links)