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Sparse graphical models via calibrated concave convex procedure with application to fMRI data

机译:稀疏的图形模型通过校准凹凸过程,其应用于FMRI数据

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ABSTRACT This paper proposes a calibrated concave convex procedure (calibrated CCCP) for high-dimensional graphical model selection. The calibrated CCCP approach for the smoothly clipped absolute deviation (SCAD) penalty is known to be path-consistent with probability converging to one in linear regression models. We implement the calibrated CCCP method with the SCAD penalty for the graphical model selection. We use a quadratic objective function for undirected Gaussian graphical models and adopt the SCAD penalty for sparse estimation. For the tuning procedure, we propose to use columnwise tuning on the quadratic objective function adjusted for test data. In a simulation study, we compare the performance of the proposed method with two existing graphical model estimators for high-dimensional data in terms of matrix error norms and support recovery rate. We also compare the bias and the variance of the estimated matrices. Then, we apply the method to functional magnetic resonance imaging (fMRI) data of an attention deficit hyperactivity disorders (ADHD) patient.
机译:摘要本文提出了一种用于高维图形模型选择的校准凹凸程序(校准CCCP)。已知平稳地剪切绝对偏差(SCAD)惩罚的校准CCCP方法是具有在线性回归模型中的概率会聚的路径一致。我们将校准的CCCP方法与图形模型选择的SCAD惩罚实施。我们对无向高斯图形模型使用二次目标函数,并采用稀疏估计的苏达斯罚款。对于调整过程,我们建议使用针对测试数据调整的二次目标函数的柱式调整。在仿真研究中,我们在矩阵误差规范方面与两个现有的图形模型估计有两个现有图形模型估计的性能,并支持恢复速率。我们还比较估计矩阵的偏差和方差。然后,我们将该方法应用于注意力缺陷多动障碍(ADHD)患者的功能磁共振成像(FMRI)数据。

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