ArticleIEEE transactions on information theory2022
Sparse Group Lasso: Optimal Sample Complexity, Convergence Rate, and Statistical Inference.
Article in IEEE transactions on information theory, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- 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
- Statistical inference on high-dimensional covariate-dependent Gaussian graphical regressions.Biometrics · 2025Article
- Debiased inference for heterogeneous subpopulations in a high-dimensional logistic regression model.Scientific reports · 2023Article
- Sparse Multicategory Generalized Distance Weighted Discrimination in Ultra-High Dimensions.Entropy (Basel, Switzerland) · 2020Article
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3 authors.
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Abstract
We study sparse group Lasso for high-dimensional double sparse linear regression, where the parameter of interest is simultaneously element-wise and group-wise sparse. This problem is an important instance of the simultaneously structured model - an actively studied topic in statistics and machine learning. In the noiseless case, matching upper and lower bounds on sample complexity are established for the exact recovery of sparse vectors and for stable estimation of approximately sparse vectors, respectively. In the noisy case, upper and matching minimax lower bounds for estimation error are obtained. We also consider the debiased sparse group Lasso and investigate its asymptotic property for the purpose of statistical inference. Finally, numerical studies are provided to support the theoretical results.
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