论文标题
具有错误发现率控制的大规模高斯图形模型中的转移学习
Transfer Learning in Large-scale Gaussian Graphical Models with False Discovery Rate Control
论文作者
论文摘要
研究了高维高斯图形模型（GGM）的转移学习，目的是通过利用来自相似和相关辅助研究的数据来估算目标GGM。目标图和每个辅助图之间的相似性的特征在于发散矩阵的稀疏性。提出了一种估计算法，即反式lime，并显示出比单个研究环境中的最小值速率更快的收敛速率。此外，引入了一个依据的反式估计量，并证明是渐近的元素正常。它用于构建一个多个测试程序，用于通过错误的发现率控制进行边缘检测。提出的估计和多个测试程序在模拟中表现出了出色的数值性能，并通过利用来自多个其他脑组织的基因表达来推断目标脑组织中的基因网络。观察到了预测误差的显着减少和链路检测功率的显着增加。
Transfer learning for high-dimensional Gaussian graphical models (GGMs) is studied with the goal of estimating the target GGM by utilizing the data from similar and related auxiliary studies. The similarity between the target graph and each auxiliary graph is characterized by the sparsity of a divergence matrix. An estimation algorithm, Trans-CLIME, is proposed and shown to attain a faster convergence rate than the minimax rate in the single study setting. Furthermore, a debiased Trans-CLIME estimator is introduced and shown to be element-wise asymptotically normal. It is used to construct a multiple testing procedure for edge detection with false discovery rate control. The proposed estimation and multiple testing procedures demonstrate superior numerical performance in simulations and are applied to infer the gene networks in a target brain tissue by leveraging the gene expressions from multiple other brain tissues. A significant decrease in prediction errors and a significant increase in power for link detection are observed.
