基于ADMM算法的网络连接数据变量选择

随着科技的发展,网络连接数据在统计学习、机器学习等领域的应用越来越普遍.在线性回归模型中,目前关于网络连接数据的变量选择研究主要针对的是同质性样本,即样本的个体效应α相同,但在现实中大多数样本的个体效应存在异质性,在不考虑异质性的情况下会使得模型的估计和预测产生较大偏差.因此,当网络数据中个体效应存在组异质性时,本文提...

Variable Selection of Network-linked Data Based on ADMM Algorithm

With the development of science and technology, the application of network-linked data in statistical learning, machine learning and other fields becomes increasingly common. In linear regression models, the current research on the variable selection of network-linked data mainly focuses on the homogeneous samples, namely that the individual effects of the samples are the same. In reality, however, the individual effects of most samples are heterogeneous. As a result, the neglect of the heterogeneity will lead to large deviations in the estimation and prediction of the models. Therefore, this paper proposes a new variable selection method SNC to cope with the situation when there is group heterogeneity in network-linked data. Using the network agglomeration effect , we carry out a joint penalty for the difference between the variable coefficient and the individual effect of the connected samples and solve the problem with ADMM algorithm, with the convergence of the algorithm proved. The results of numerical simulation and example analysis show that this method improves the accuracy of variable selection and reduces the prediction error.