VARIABLE SELECTION FOR HIGH DIMENSIONAL MULTIVARIATE OUTCOMES.
VARIABLE SELECTION FOR HIGH DIMENSIONAL MULTIVARIATE OUTCOMES.
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DOI：
10.5705/ss.2013.019
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发表时间：
2014-10
影响因子：
1.4
通讯作者：
Lin X
中科院分区：
文献类型：
--
作者：
Sofer T;Dicker L;Lin X
当结果数和协变量数可能很大时，我们考虑使用惩罚似然的高维多变量回归的变量选择。为了说明受试者内的相关性，我们考虑变量选择时，使用的工作精度矩阵和精度矩阵联合估计时，使用两个阶段的程序。我们证明了在适当的正则性条件下，惩罚回归系数估计对于任意工作精度矩阵的模型选择是一致的，并且具有预言性质，并且当使用真实精度矩阵或使用稀疏回归一致估计时是有效的。我们开发了一个有效的计算程序，估计回归系数使用坐标下降算法结合稀疏精度矩阵估计使用图形LASSO（GLASSO）算法。我们开发的贝叶斯信息准则（BIC）估计的调整参数，并表明BIC是一致的模型选择。我们评估有限样本的性能，所提出的方法使用模拟研究，并说明其应用程序使用II型糖尿病基因表达途径的数据。
We consider variable selection for high-dimensional multivariate regression using penalized likelihoods when the number of outcomes and the number of covariates might be large. To account for within-subject correlation, we consider variable selection when a working precision matrix is used and when the precision matrix is jointly estimated using a two-stage procedure. We show that under suitable regularity conditions, penalized regression coefficient estimators are consistent for model selection for an arbitrary working precision matrix, and 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 pathway data.