Regularized estimation in sparse high-dimensional multivariate regression, with application to a DNA methylation study

Haixiang Zhang, Yinan Zheng, Grace Yoon, Zhou Zhang, Tao Gao, Brian Joyce, Wei Zhang, Joel Schwartz, Pantel Vokonas, Elena Colicino, Andrea Baccarelli, Lifang Hou, Lei Liu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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 ℓ 1 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).

Original languageEnglish (US)
Pages (from-to)159-171
Number of pages13
JournalStatistical Applications in Genetics and Molecular Biology
Volume16
Issue number3
DOIs
StatePublished - Jul 26 2017

Keywords

  • high-dimensional responses
  • multivariate regression
  • oracle inequality
  • tuning-insensitive
  • weighted square-root LASSO

ASJC Scopus subject areas

  • Statistics and Probability
  • Molecular Biology
  • Genetics
  • Computational Mathematics

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