论文检索

识别二元选择中的异质性社会效应AI 解读

We identify and estimate heterogeneous social effects within groups of individuals that make binary choices. These heterogeneous social effects, which include peer and contextual effects, are modeled through unobserved influence matrices that summarize how the members within each group affect each other’s outcomes. We recover parameters in social effects as well as the unknown influence matrices by exploiting how these matrices are linked to the reduced-form effects of…

网络依赖随机变量的极限定理AI 解读

This paper is concerned with cross-sectional dependence arising because observations are interconnected through an observed network. Following (Doukhan and Louhichi, 1999), we measure the strength of dependence by covariances of nonlinearly transformed variables. We provide a law of large numbers and central limit theorem for network dependent variables. We also provide a method of calculating standard errors robust to general forms of network dependence. For that purpose,…

鞅差下有效的两步广义经验似然估计与检验AI 解读

This paper considers two-step generalized empirical likelihood (GEL) estimation and tests with martingale differences when there is a computationally simple $\sqrt n$ -consistent estimator of nuisance parameters or the nuisance parameters can be eliminated with an estimating function of parameters of interest. As an initial estimate might have asymptotic impact on final estimates, we propose general $C(\alpha )$ -type transformed moments to eliminate the impact, and use them…

差异潜变量图模型估计及其脑连接应用AI 解读

Summary Differential graphical models are designed to represent the difference between the conditional dependence structures of two groups, and thus are of particular interest for scientific investigations. Motivated by modern applications, this manuscript considers an extended setting where each group is generated by a latent variable Gaussian graphical model. Due to the existence of latent factors, the differential network is decomposed into sparse and low-rank components,…

线性约束下向量自回归模型的有限时间分析AI 解读

Summary This paper develops a unified finite-time theory for the ordinary least squares estimation of possibly unstable and even slightly explosive vector autoregressive models under linear restrictions, with the applicable region $\rho(A)\leqslant 1+c/n$, where $\rho(A)$ is the spectral radius of the transition matrix $A$ in the var(1) representation, $n$ is the time horizon and $c>0$ is a universal constant. The linear restriction framework encompasses various existing…

内生社会网络中同伴效应的控制函数估计AI 解读

Abstract We propose methods of estimating the linear-in-means model of peer effects in which the peer group, defined by a social network, is endogenous in the outcome equation for peer effects. Endogeneity is due to unobservable individual characteristics that influence both link formation in the network and the outcome of interest. We propose two estimators of the peer effect equation that control for the endogeneity of the social connections using a control function…

矩阵值时间序列的自回归模型AI 解读

In finance, economics and many other fields, observations in a matrix form are often generated over time. For example, a set of key economic indicators are regularly reported in different countries every quarter. The observations at each quarter neatly form a matrix and are observed over consecutive quarters. Dynamic transport networks with observations generated on the edges can be formed as a matrix observed over time. Although it is natural to turn the matrix observations…

网络因果效应的自动 g 计算AI 解读

Methods for inferring average causal effects have traditionally relied on two key assumptions: (i) the intervention received by one unit cannot causally influence the outcome of another; and (ii) units can be organized into nonoverlapping groups such that outcomes of units in separate groups are independent. In this article, we develop new statistical methods for causal inference based on a single realization of a network of connected units for which neither assumption (i)…

同伴效应观察性研究中的偏差与高维调整AI 解读

Peer effects, in which an individual’s behavior is affected by peers’ behavior, are posited by multiple theories in the social sciences. Randomized field experiments that identify peer effects, however, are often expensive or infeasible, so many studies of peer effects use observational data, which is expected to suffer from confounding. Here we show, in the context of information and media diffusion, that high-dimensional adjustment of a nonexperimental control group (660…

多个脑功能网络的贝叶斯联合建模AI 解读

Investigating the similarity and changes in brain networks under different mental conditions has become increasingly important in neuroscience research. A standard separate estimation strategy fails to pool information across networks and hence has reduced estimation accuracy and power to detect between-network differences. Motivated by a fMRI Stroop task experiment that involves multiple related tasks, we develop an integrative Bayesian approach for jointly modeling…

两阶段随机实验中存在干扰与不依从时的因果推断AI 解读

In many social science experiments, subjects often interact with each other and as a result one unit’s treatment influences the outcome of another unit. Over the last decade, a significant progress has been made toward causal inference in the presence of such interference between units. Researchers have shown that the two-stage randomization of treatment assignment enables the identification of average direct and spillover effects. However, much of the literature has assumed…

多层基因组网络中的贝叶斯结构学习AI 解读

Integrative network modeling of data arising from multiple genomic platforms provides insight into the holistic picture of the interactive system, as well as the flow of information across many disease domains including cancer. The basic data structure consists of a sequence of hierarchically ordered datasets for each individual subject, which facilitates integration of diverse inputs, such as genomic, transcriptomic, and proteomic data. A primary analytical task in such…

以无向网络为预测变量的贝叶斯回归：脑连接组应用AI 解读

This article focuses on the relationship between a measure of creativity and the human brain network for subjects in a brain connectome dataset obtained using a diffusion weighted magnetic resonance imaging procedure. We identify brain regions and interconnections that have a significant effect on creativity. Brain networks are often expressed in terms of symmetric adjacency matrices, with row and column indices of the matrix representing the regions of interest (ROI), and a…

网络观察性研究中处理效应与干扰效应的识别和估计AI 解读

Laura Forastierea , Edoardo M. Airoldib* & Fabrizia Meallica Department of Biostatistics, Yale University, New Haven, CTb Department of Statistical Science, Fox School of Business, Temple University, Philadelphia, PAc Department of Statistics, Informatics, Applications “G. Parenti”, University of Florence, Florence, ItalySupplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA.CONTACT Edoardo M. Airoldi airoldi@temple.edu…

稀疏图中协变量正则化的社区发现AI 解读

In this article, we investigate community detection in networks in the presence of node covariates. In many instances, covariates and networks individually only give a partial view of the cluster structure. One needs to jointly infer the full cluster structure by considering both. In statistics, an emerging body of work has been focused on combining information from both the edges in the network and the node covariates to infer community memberships. However, so far the…

感染图的置换检验AI 解读

We formulate and analyze a novel hypothesis testing problem for inferring the edge structure of an infection graph. In our model, a disease spreads over a network via contagion or random infection, where the times between successive contagion events are independent exponential random variables with unknown rate parameters. A subset of nodes is also censored uniformly at random. Given the observed infection statuses of nodes in the network, the goal is to determine the…

区分个体处理效应与溢出效应的框架AI 解读

This article suggests a causal framework for separating individual-level treatment effects and spillover effects such as general equilibrium, interference, or interaction effects related to treatment distribution. We relax the stable unit treatment value assumption assuming away treatment-dependent interaction between study participants and permit spillover effects within aggregates, for example, regions. Based on our framework, we systematically categorize the…

上游与下游：通用产品代码的扩散及其影响AI 解读

We study the adoption, diffusion, and impacts of the Universal Product Code (UPC) between 1975 and 1992, during the initial years of the bar code system. We find evidence of network effects in the diffusion process. Matched-sample difference-in-differences estimates show that firm size and trademark registrations increase following UPC adoption by manufacturers. Industry-level import penetration also increases with domestic UPC adoption. Our findings suggest that bar codes,…

复杂区域上的非参数密度估计AI 解读

Abstract We propose a nonparametric method for density estimation over (possibly complicated) spatial domains. The method combines a likelihood approach with a regularization based on a differential operator. We demonstrate the good inferential properties of the method. Moreover, we develop an estimation procedure based on advanced numerical techniques, and in particular making use of finite elements. This ensures high computational efficiency and enables great flexibility.…

尖峰可分离协方差矩阵与主成分AI 解读

We study a class of separable sample covariance matrices of the form Q˜1:=A˜1/2XB˜X∗A˜1/2. Here, A˜ and B˜ are positive definite matrices whose spectrums consist of bulk spectrums plus several spikes, that is, larger eigenvalues that are separated from the bulks. Conceptually, we call Q˜1 a spiked separable covariance matrix model. On the one hand, this model includes the spiked covariance matrix as a special case with B˜=I. On the other hand, it allows for more general…

可交换数组的经验过程结果AI 解读

Exchangeable arrays are natural tools to model common forms of dependence between units of a sample. Jointly exchangeable arrays are well suited to dyadic data, where observed random variables are indexed by two units from the same population. Examples include trade flows between countries or relationships in a network. Separately exchangeable arrays are well suited to multiway clustering, where units sharing the same cluster (e.g., geographical areas or sectors of activity…

稀疏高维变点检测的极小极大速率AI 解读

We study the detection of a sparse change in a high-dimensional mean vector as a minimax testing problem. Our first main contribution is to derive the exact minimax testing rate across all parameter regimes for n independent, p-variate Gaussian observations. This rate exhibits a phase transition when the sparsity level is of order ploglog(8n) and has a very delicate dependence on the sample size: in a certain sparsity regime, it involves a triple iterated logarithmic factor…

利用图根分布表示网络AI 解读

Exchangeable random graphs serve as an important probabilistic framework for the statistical analysis of network data. In this work, we develop an alternative parameterization for a large class of exchangeable random graphs, where the nodes are independent random vectors in a linear space equipped with an indefinite inner product, and the edge probability between two nodes equals the inner product of the corresponding node vectors. Therefore, the distribution of exchangeable…

未知干扰下的平均处理效应AI 解读

We investigate large-sample properties of treatment effect estimators under unknown interference in randomized experiments. The inferential target is a generalization of the average treatment effect estimand that marginalizes over potential spillover effects. We show that estimators commonly used to estimate treatment effects under no interference are consistent for the generalized estimand for several common experimental designs under limited but otherwise arbitrary and…