ArticleStatistical applications in genetics and molecular biology2017
Regularized estimation in sparse high-dimensional multivariate regression, with application to a DNA methylation study.
Article in Statistical applications in genetics and molecular biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Toward the Use of Real-World Data for Regulatory and HTA Decision-Making: Experiences and Recommendations from Six EU-Funded Projects.Clinical pharmacology and therapeutics · 2026Review
- A Novel Method for Identifying a Parsimonious and Accurate Predictive Model for Multiple Clinical Outcomes.Computer methods and programs in biomedicine · 2021Article
- Identifying factors associated with opioid cessation in a biracial sample using machine learning.Exploration of medicine · 2020Article
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Authors and funding
13 authors.
Funding
Abstract
In this article, we consider variable selection for correlated high dimensional DNA methylation markers as multivariate outcomes. A novel weighted square-root LASSO procedure is proposed to estimate the regression coefficient matrix. A key feature of this method is tuning-insensitivity, which greatly simplifies the computation by obviating cross validation for penalty parameter selection. A precision matrix obtained via the constrained ℓ1 minimization method is used to account for the within-subject correlation among multivariate outcomes. Oracle inequalities of the regularized estimators are derived. The performance of our proposed method is illustrated via extensive simulation studies. We apply our method to study the relation between smoking and high dimensional DNA methylation markers in the Normative Aging Study (NAS).
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