ArticleBiometrics2015
Multivariate sparse group lasso for the multivariate multiple linear regression with an arbitrary group structure.
Article in Biometrics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- MODEL-FREE INFERENCE FOR CHARACTERIZING PROTEIN MUTATIONS THROUGH A COEVOLUTIONARY LENS.The annals of applied statistics · 2026Article
- Multi-Platform Multivariate Regression with Group Sparsity for High-Dimensional Data Integration.Entropy (Basel, Switzerland) · 2026Article
- Multivariate Bayesian variable selection for multi-trait genetic fine mapping.Journal of the Royal Statistical Society. Series C, Applied statistics · 2025Article
- Multi-task Learning for Gaussian Graphical Regressions with High Dimensional Covariates.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2024Article
- Deep network embedding with dimension selection.Neural networks : the official journal of the International Neural Network Society · 2024Article
- Molecular group and correlation guided structural learning for multi-phenotype prediction.Briefings in bioinformatics · 2024Article
- On the Use of Minimum Penalties in Statistical Learning.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2024Article
- Debiased inference for heterogeneous subpopulations in a high-dimensional logistic regression model.Scientific reports · 2023Article
- New statistical selection method for pleiotropic variants associated with both quantitative and qualitative traits.BMC bioinformatics · 2023Article
- Factors associated with healthy aging in Latin American populations.Nature medicine · 2023Article
- Doubly structured sparsity for grouped multivariate responses with application to functional outcome score modeling.Statistics in medicine · 2023Article
- Neuroimaging feature extraction using a neural network classifier for imaging genetics.BMC bioinformatics · 2023Article
- Clinical Risk Factors For Kidney Tubule Biomarker Abnormalities Among Hypertensive Adults With Reduced eGFR in the SPRINT Trial.American journal of hypertension · 2022Article
- High-dimension to high-dimension screening for detecting genome-wide epigenetic and noncoding RNA regulators of gene expression.Bioinformatics (Oxford, England) · 2022Article
- Sparse Group Lasso: Optimal Sample Complexity, Convergence Rate, and Statistical Inference.IEEE transactions on information theory · 2022Article
- Allostatic-Interoceptive Overload in Frontotemporal Dementia.Biological psychiatry · 2022Article
- Feature selection for kernel methods in systems biology.NAR genomics and bioinformatics · 2022Article
- Evaluating physical urban features in several mental illnesses using electronic health record data.Frontiers in digital health · 2022Article
- Associations of CKD risk factors and longitudinal changes in urine biomarkers of kidney tubules among women living with HIV.BMC nephrology · 2021Article
- Kidney tubule health scores and their associations with incident CKD in women living with HIV.HIV medicine · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
We propose a multivariate sparse group lasso variable selection and estimation method for data with high-dimensional predictors as well as high-dimensional response variables. The method is carried out through a penalized multivariate multiple linear regression model with an arbitrary group structure for the regression coefficient matrix. It suits many biology studies well in detecting associations between multiple traits and multiple predictors, with each trait and each predictor embedded in some biological functional groups such as genes, pathways or brain regions. The method is able to effectively remove unimportant groups as well as unimportant individual coefficients within important groups, particularly for large p small n problems, and is flexible in handling various complex group structures such as overlapping or nested or multilevel hierarchical structures. The method is evaluated through extensive simulations with comparisons to the conventional lasso and group lasso methods, and is applied to an eQTL association study.
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Registered trials
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.