论文标题
彩色图形高斯模型的惩罚复合可能性
Penalized composite likelihood for colored graphical Gaussian models
论文作者
论文摘要
本文提出了一种用于彩色图形高斯模型模型选择的惩罚复合可能性方法。该方法提供了精确矩阵的稀疏和对称约束的估计器，因此同时进行了模型选择和精度矩阵估计。特别是，该方法使用惩罚术语来限制精度矩阵的元素，这使我们能够将模型选择问题转换为受约束的优化问题。此外，进行了计算机实验，以说明拟议的新方法的性能。结果表明，所提出的方法在精确矩阵中非零元素的选择和图形模型中对称结构的识别均表现良好。该方法的可行性和潜在临床应用在微阵列基因表达数据集中证明。
This paper proposes a penalized composite likelihood method for model selection in colored graphical Gaussian models. The method provides a sparse and symmetry-constrained estimator of the precision matrix, and thus conducts model selection and precision matrix estimation simultaneously. In particular, the method uses penalty terms to constrain the elements of the precision matrix, which enables us to transform the model selection problem into a constrained optimization problem. Further, computer experiments are conducted to illustrate the performance of the proposed new methodology. It is shown that the proposed method performs well in both the selection of nonzero elements in the precision matrix and the identification of symmetry structures in graphical models. The feasibility and potential clinical application of the proposed method are demonstrated on a microarray gene expression data set.
