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混合成员模型中混合概率与排序的统计推断AI 解读

Network data is prevalent in numerous big data applications, including economics and health networks, where understanding the latent structure of the network is of prime importance. In this paper, we model the network using the Degree-Corrected Mixed Membership (DCMM) model. In the DCMM model, for each node i, there exists a membership vector πi=(πi(1),πi(2),…,πi(K)), where πi(k) denotes the weight that node i puts in community k. We derive a novel finite-sample expansion…

网络干扰下基于平均场方法的因果效应估计AI 解读

We study causal effect estimation from observational data under interference. The interference pattern is captured by an observed network. We adopt the chain graph framework of (J. Amer. Statist. Assoc. 116 (2021) 833–844), which allows (i) interaction among the outcomes of distinct study units connected along the graph and (ii) long range interference, whereby the outcome of an unit may depend on the treatments assigned to distant units connected along the interference…

成对依赖马尔可夫随机场中的虚假关联AI 解读

Summary identified the issue of ′nonsense correlations’ in time series data, where dependence within each of two random vectors causes overdispersion, i.e., variance inflation, for measures of dependence between the two. Since then much has been written about nonsense correlations, but nearly all of it confined to the time series literature. In this paper we provide the first, to our knowledge, rigorous study of this phenomenon for other forms of (positive) dependence,…