BUGS embeds univariate marginal guidance into a regularized horseshoe prior to induce adaptive shrinkage, supplies theoretical contraction guarantees, and offers an active-set MCMC approximation that scales to p=1,000,000 while improving false-discovery control.
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Regularization and Variable Selection Via the Elastic Net
11 Pith papers cite this work. Polarity classification is still indexing.
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2026 11representative citing papers
Proposes an inferential framework to test differences in categorical Gini correlations for predictor importance in classification, establishing asymptotic normality and consistency while accommodating unequal dimensions and dependence.
A new geographically weighted penalized compositional regression model with pairwise fusion penalty is proposed to handle spatial heterogeneity and compositional covariates, demonstrated on U.S. income and COPD data.
An LLM-based topic modeling method with a custom evaluation framework improves topic interpretability, specificity, and polarity consistency over prior approaches when linking corporate review text to external outcomes such as employee morale.
An AI recommender system improves Cox Proportional Hazards model performance for predicting patient falls by suggesting 23 feature exclusions, 2 non-linear terms, and 221 interactions, raising C-index from 0.805 to 0.815.
A weighted K-means plus decision-tree pipeline learns multi-action policies from observational data and is applied to HCV treatment choices for HIV co-infected patients, finding a high-clearance subgroup and potential cost savings of CAN$3.6-4.9 million.
GSA-YOLO modifies YOLOv8n with structured sparsity via Group Lasso and Sparse Structure Selection plus Adaptive Knowledge Distillation, reporting 189.62 FPS and mAP50:95 gains of 2.4% and 1.8% on HiXray and PIDray datasets.
Simulations show Ridge, Lasso, and ElasticNet perform similarly for prediction at high sample-to-feature ratios, but Lasso feature selection recall drops to 0.18 under high multicollinearity and low SNR while ElasticNet holds at 0.93.
Ensemble learning with Gaussian copula transformation predicts groundwater heavy metal pollution index with high accuracy (R²=0.96) while identifying key contaminants via clustering.
fastml is an R package that enforces leakage-free preprocessing through guarded resampling and provides a unified interface for safer automated ML including survival analysis.