ArticleStatistical science : a review journal of the Institute of Mathematical Statistics2012
A Selective Review of Group Selection in High-Dimensional Models.
Article in Statistical science : a review journal of the Institute of Mathematical Statistics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers.
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Who cites it
82 citing papers in PubMed.
- Multi-task learning for sparsity pattern heterogeneity: statistical and computational perspectives.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2026Article
- Nonlinear kernel-based high-dimensional inference for set-based genetic association studies.Briefings in bioinformatics · 2026Article
- Enhancing cancer prognostics with group penalty models: a comparative study on radiomics feature selection in lung adenocarcinomas and meningiomas.Quantitative imaging in medicine and surgery · 2026Article
- Hierarchical structure-guided high-dimensional multi-view clustering.Journal of multivariate analysis · 2026Article
- Sparse-Input Neural Network using Group Concave Regularization.Transactions on machine learning research · 2025Article
- A Systematic Intelligent Optimization Framework for a Sustained-Release Formulation Design.Pharmaceutics · 2025Article
- GRASS-NB: Group-structured variable selection for spatial negative binomial data with applications to cancer registry and spatial omics.bioRxiv : the preprint server for biology · 2025Article
- Variable Selection for Multivariate Failure Time Data via Regularized Sparse-Input Neural Network.Bioengineering (Basel, Switzerland) · 2025Article
- Multivariate Bayesian variable selection for multi-trait genetic fine mapping.Journal of the Royal Statistical Society. Series C, Applied statistics · 2025Article
- A group penalization framework for detecting time-lagged microbiota-host associations.Frontiers in genetics · 2025Article
- The spike-and-slab quantile LASSO for robust variable selection in cancer genomics studies.Statistics in medicine · 2024Article
- Is Seeing Believing? A Practitioner's Perspective on High-Dimensional Statistical Inference in Cancer Genomics Studies.Entropy (Basel, Switzerland) · 2024Review
- A high-dimensional omnibus test for set-based association analysis.Briefings in bioinformatics · 2024Article
- A novel body composition risk score (B-Score) and overall survival among patients with nonmetastatic breast cancer.Clinical nutrition (Edinburgh, Scotland) · 2024Article
- scFSNN: a feature selection method based on neural network for single-cell RNA-seq data.BMC genomics · 2024Article
- Article
- A penalized variable selection ensemble algorithm for high-dimensional group-structured data.PloS one · 2024Article
- Low-rank regression models for multiple binary responses and their applications to cancer cell-line encyclopedia data.Journal of the American Statistical Association · 2024Article
- Partially constrained group variable selection to adjust for complementary unit performance in American college football.Journal of applied statistics · 2024Article
- Simultaneous selection and inference for varying coefficients with zero regions: a soft-thresholding approach.Biometrics · 2023Article
22 more citing papers are in PubMed but not listed here.
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3 authors.
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Abstract
Grouping structures arise naturally in many statistical modeling problems. Several methods have been proposed for variable selection that respect grouping structure in variables. Examples include the group LASSO and several concave group selection methods. In this article, we give a selective review of group selection concerning methodological developments, theoretical properties and computational algorithms. We pay particular attention to group selection methods involving concave penalties. We address both group selection and bi-level selection methods. We describe several applications of these methods in nonparametric additive models, semiparametric regression, seemingly unrelated regressions, genomic data analysis and genome wide association studies. We also highlight some issues that require further study.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.