Multivariate sparse group lasso for the multivariate multiple linear regression with an arbitrary group structure
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Publication:3459931
DOI10.1111/biom.12292zbMath1390.62285OpenAlexW1560240297WikidataQ35778722 ScholiaQ35778722MaRDI QIDQ3459931
Publication date: 11 January 2016
Published in: Biometrics (Search for Journal in Brave)
Full work available at URL: http://europepmc.org/articles/pmc4479976
high-dimensional datasparsityoracle inequalitiescoordinate descent algorithmgenetic associationeQTLmultivariate sparse group Lasso variable selection
Estimation in multivariate analysis (62H12) Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12)
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Cites Work
- Unnamed Item
- A sparse conditional Gaussian graphical model for analysis of genetical genomics data
- The Adaptive Lasso and Its Oracle Properties
- Group variable selection via a hierarchical lasso and its oracle property
- Optimal selection of reduced rank estimators of high-dimensional matrices
- Oracle inequalities and optimal inference under group sparsity
- Regularized multivariate regression for identifying master predictors with application to integrative genomics study of breast cancer
- Multiple hypothesis testing in microarray experiments.
- Simultaneous analysis of Lasso and Dantzig selector
- Support union recovery in high-dimensional multivariate regression
- Coordinate descent algorithms for lasso penalized regression
- Logistic Bayesian LASSO for Identifying Association with Rare Haplotypes and Application to Age-Related Macular Degeneration
- A group bridge approach for variable selection
- Penalized logistic regression for detecting gene interactions
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Regularization and Variable Selection Via the Elastic Net
- Model Selection and Estimation in Regression with Grouped Variables
- Convergence of a block coordinate descent method for nondifferentiable minimization