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Activity Number: 555 - Statistical Analysis of Epigenetics Data
Type: Invited
Date/Time: Wednesday, August 2, 2017 : 2:00 PM to 3:50 PM
Sponsor: American Association for the Advancement of Science
Abstract #321858 View Presentation
Title: Regularized Estimation in Sparse Multivariate Regression for High-Dimensional DNA Methylation Data
Author(s): Lei Liu* and Haixiang Zhang and Grace Yoon and Yinan Zheng and Lifang Hou and Wei Zhang and Andrea Baccarelli
Companies: Northwestern University and Tianjin University and Northwestern University and Northwestern University and Northwestern University and Northwestern University and Columbia University
Keywords: High-dimensional responses ; Multivariate regression ; Tuning-insensitive ; Weighted square-root LASSO

In this article, we propose a new weighted square-root LASSO procedure to estimate the regression coefficient matrix in a sparse multivariate regression model with high-dimensional responses from DNA methylation data. A key feature of this method is tuning-insensitivity, which greatly simplifies the computation by obviating the cross validation for penalty parameter selection. A working precision matrix is used to account for within-subject correlations among responses. Oracle inequalities of the regularized estimators are derived. The performance of our proposed methodology is illustrated via extensive simulation studies. An application to DNA methylation data in the normative aging study is provided.

Authors who are presenting talks have a * after their name.

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