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Activity Number: 77
Type: Contributed
Date/Time: Sunday, July 29, 2012 : 4:00 PM to 5:50 PM
Sponsor: Biometrics Section
Abstract - #304290
Title: Variable Selection for High-Dimensional Multivariate Outcomes with Application to Genetic Pathway/Network Analysis
Author(s): Tamar Sofer*+ and Lee Dicker and Xihong Lin
Companies: Harvard School of Public Health and Rutgers University and Harvard University
Address: 655 Huntington Avenue, Boston, MA, 02115, United States
Keywords: Efficiency ; Multiple outcomes ; Gene set analysis ; Joint estimation ; Model selection
Abstract:

We consider variable selection for high-dimensional multivariate regression using penalized likelihood when the number of outcomes and the number of covariates might be large. To account for within-subject correlation, either a working precision matrix is used or a precision matrix is jointly estimated using a two-stage procedure. Under suitable regularity conditions, the penalized regression coefficient estimators are consistent for model selection for an arbitrary working precision matrix, have the oracle properties and are efficient when the true precision matrix is used or when it is consistently estimated using sparse regression. We develop an efficient computation procedure for estimating regression coefficients using the coordinate descent algorithm in conjunction with sparse precision matrix estimation using the graphical LASSO (GLASSO) algorithm. We develop the Bayesian Information Criterion (BIC) for estimating the tuning parameter and show that BIC is consistent for model selection. We evaluate finite sample performance for the proposed method using simulation studies and illustrate its application using the type II diabetes gene expression data set.


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