A Statistical Model of the Gross Analysis of Transaction Flows
交易流总体分析的统计模型AI 解读 ↓
本页整理公开书目信息,不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成,未完成全文核验,可能有误;具体假设、证明与结论请核对原文。摘要保留来源文本,未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要;缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录,不合计多个记录;关联记录可能包含正式发表版本,不等于此版本单独引用。缺失不视为零,引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配,同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列,再参考通用方法与发表日期。
AI 根据所提供的论文材料归类:摘要以参与者之间的有向交易矩阵、群体聚类与整合为研究对象,为关系网络分析提供了直接的文本依据。
反馈分类问题交易流总体分析的统计模型AI 解读 ↓
正在核对关联版本…
正在核对对应版本的资源…
This is applicable to the flow of any kind of quantifiable transactions, and to matrices from three to more than one hundred actors, utilizing a 650 IBM electronic computer or similar equipment. The method develops a matrix of expected or baseline data from assumptions of complete indifference among the actors, and measures the plus or minus differences between this baseline value and the actual amount of transactions in each direction for every pair of actors. It thus removes gross size effects and permits tentative inferences about the distribution of preferences among pairs of larger groups of actors; about degrees of clustering or integration among actors; and about changes over time, if several matrices are used. It thus locates interesting pairs or groups for further study. Import-export data are used as an example to show the detailed application of the method. For a given year and a group of countries, the importexport data can be arranged like a contingency table, except the diagonal cells are zero and the other entries are quantities of money, a continuous variable instead of a discrete variable. A describing the data and techniques for the statistical analysis are presented. This is a null model in the sense that the departures from it are of primary interest. The North Atlantic Area (1928) is used as an illustration.
Predict-then-optimize methods learn a mapping from features to unknown edge costs. They then solve the shortest-path problem on the predicted costs. We study a feature-free alternative for the case where the decision-maker observes many noisy cost vectors on a fixed network. The cost vectors are denoised by an eigenvalue decomposition (PCA) of their covariance matrix. Dijkstra's algorithm then runs on the result. We test this on the $5\times5$ grid of \citet{elmachtoub2022},…
社会网络数据中处理效应的识别与估计:当社会连接存在混杂时
In observational network data, the adoption of a treatment and the formation of social ties often depend on the same characteristics that affect outcomes. We propose a generalized propensity score for each unit's effective treatment, built from a model for treatment assignment and a dyadic model for network formation, which may include latent node heterogeneity. The score is marginalized over the network given covariates rather than conditioned on the realized network, and…
双重高维线性混合模型下异质图模型的推断
Inferring the structure of population-level functional connectivity networks from multi-subject functional magnetic resonance imaging data is essential for understanding the connectivity patterns underlying cognitive and behavioral functions. Graphical models provide a formal statistical framework for inferring the graph structure. However, the multi-level nature of functional magnetic resonance imaging data---where each level corresponds, for instance, to a…
复杂(准)实验设计下的简单标准误
We develop standard errors for estimates of causal effects in settings where the distribution of treatment exposures is known but potentially exhibits complicated dependence patterns across units. Such dependence arises, for example, in settings with spillovers/interference across units or when the treatment assignment is spatially correlated. Our variance estimator generalizes several properties of the Neyman variance estimator for completely randomized experiments to…
We develop inference for complete dyadic samples with spatial dependence governed by geographic distances between nodes, covering settings beyond the scope of existing dyadic inference theory. We propose spatial variance and corrected block jackknife estimators consistent in nondegenerate and degenerate Gaussian cases. Under degeneracy, spatial subsampling consistently estimates possibly non-Gaussian limits. Combining the jackknife with subsampling yields the max…
通过模型平均实现多层网络迁移学习的框架
Link prediction in multilayer networks is a key challenge in applications such as recommendation systems and protein-protein interaction prediction. While many techniques have been developed, most rely on assumptions about shared structures and require access to raw auxiliary data, limiting their practicality. To address these issues, we propose a novel transfer learning framework for multilayer networks using a bi-level model averaging method. A $K$-fold cross-validation…