ArticleBiostatistics (Oxford, England)2020
The joint lasso: high-dimensional regression for group structured data.
Article in Biostatistics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Extensions of Heterogeneity in Integration and Prediction (HIP) With R Shiny Application.Statistics in medicine · 2025Article
- THOR: a TMB heterogeneity-adaptive optimization model predicts immunotherapy response using clonal genomic features in group-structured data.Briefings in bioinformatics · 2024Article
- HIP: a method for high-dimensional multi-view data integration and prediction accounting for subgroup heterogeneity.Briefings in bioinformatics · 2024Article
- Targeted co-expression networks for the study of traits.Scientific reports · 2024Article
- Estimation and Inference for High-Dimensional Generalized Linear Models with Knowledge Transfer.Journal of the American Statistical Association · 2024Article
- Driving forces of Antarctic krill abundance.Science advances · 2023Article
- Objective study of the facial parameters of observations in patients with type 2 diabetes mellitus by machine learning.Annals of translational medicine · 2022Article
- Transfer Learning for High-Dimensional Linear Regression: Prediction, Estimation and Minimax Optimality.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2022Article
- Adaptive group-regularized logistic elastic net regression.Biostatistics (Oxford, England) · 2021Article
- Multi-tissue transcriptome-wide association studies.Genetic epidemiology · 2021Article
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3 authors.
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Abstract
We consider high-dimensional regression over subgroups of observations. Our work is motivated by biomedical problems, where subsets of samples, representing for example disease subtypes, may differ with respect to underlying regression models. In the high-dimensional setting, estimating a different model for each subgroup is challenging due to limited sample sizes. Focusing on the case in which subgroup-specific models may be expected to be similar but not necessarily identical, we treat subgroups as related problem instances and jointly estimate subgroup-specific regression coefficients. This is done in a penalized framework, combining an $\ell_1$ term with an additional term that penalizes differences between subgroup-specific coefficients. This gives solutions that are globally sparse but that allow information-sharing between the subgroups. We present algorithms for estimation and empirical results on simulated data and using Alzheimer's disease, amyotrophic lateral sclerosis, and cancer datasets. These examples demonstrate the gains joint estimation can offer in prediction as well as in providing subgroup-specific sparsity patterns.
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