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函数型数据的 Copula 高斯图模型AI 解读

We introduce a statistical graphical model for multivariate functional data, which are common in medical applications such as EEG and fMRI. Recently published functional graphical models rely on the multivariate Gaussian process assumption, but we relax it by introducing the functional copula Gaussian graphical model (FCGGM). This model removes the marginal Gaussian assumption but retains the simplicity of the Gaussian dependence structure, which is particularly attractive…

《统计学家的共同被引与共同署名网络》答辩回应AI 解读

We thank David Donoho for very encouraging comments. As always, his penetrating vision and deep thoughts are extremely stimulating. We are glad that he summarizes a major philosophical difference b...

关于《统计学家的共同被引与共同署名网络》的讨论AI 解读

We congratulate the authors for their stimulating and thought-provoking work on network data analysis. In the article, the authors not only introduce a new large-scale and high-quality publication dataset that will surely become an important benchmark for further network research, but also present novel statistical methods and modeling which lead to very interesting findings about the statistics community. There is much material for thought and exploration. In this…

《统计学家的共引与合著网络》讨论AI 解读

We want to congratulate the authors on a fascinating article containing an insightful analysis and their hard work curating the high-quality co-citation and co-authorship networks. These datasets a...

《统计学家的共引与合著网络》讨论AI 解读

1. We thank the authors for their new contribution to a high quality dataset and interesting findings from the modeling and analysis of the co-citation and co-authorship networks of statisticians. ...

变系数面板数据模型的网络聚类方法AI 解读

In this article, we introduce a novel varying-coefficient panel-data model with locally stationary regressors and unknown group structure, in which the number of groups and the group membership are left unspecified. We develop a triple-localization approach to estimate the unknown subject-specific coefficient functions and then identify the latent group structure via community detection. To improve the efficiency of the first-stage estimator, we further propose a two-stage…

基于多类连通矩阵凸组合的空间自回归模型贝叶斯平均AI 解读

There is a great deal of literature regarding use of nongeographically based connectivity matrices or combinations of geographic and non-geographic structures in spatial econometric models. We focus on convex combinations of weight matrices that result in a single weight matrix reflecting multiple types of connectivity, where coefficients from the convex combination can be used for inference regarding the relative importance of each type of connectivity in the global…

SIMPLE：大型网络中社群成员构成的统计推断AI 解读

Abstract Network data are prevalent in many contemporary big data applications in which a common interest is to unveil important latent links between different pairs of nodes. Yet a simple fundamental question of how to precisely quantify the statistical uncertainty associated with the identification of latent links still remains largely unexplored. In this paper, we propose the method of statistical inference on membership profiles in large networks (SIMPLE) in the setting…

函数型结构方程模型AI 解读

Abstract In this article, we introduce a functional structural equation model for estimating directional relations from multivariate functional data. We decouple the estimation into two major steps: directional order determination and selection through sparse functional regression. We first propose a score function at the linear operator level, and show that its minimization can recover the true directional order when the relation between each function and its parental…

通过子图抽样估计网络模体：四阶矩现象AI 解读

Network sampling is an indispensable tool for understanding features of large complex networks where it is practically impossible to search over the entire graph. In this paper, we develop a framework for statistical inference for counting network motifs, such as edges, triangles and wedges, in the widely used subgraph sampling model, where each vertex is sampled independently, and the subgraph induced by the sampled vertices is observed. We derive necessary and sufficient…

网络矩统计量的 Edgeworth 展开AI 解读

Network method of moments (Ann. Statist. 39 (2011) 2280–2301) is an important tool for nonparametric network inference. However, there has been little investigation on accurate descriptions of the sampling distributions of network moment statistics. In this paper, we present the first higher-order accurate approximation to the sampling CDF of a studentized network moment by Edgeworth expansion. In sharp contrast to classical literature on noiseless U-statistics, we show that…

企业财务政策中的网络效应AI 解读

We present a spatial econometrics framework for estimating peer effects in capital structure. This approach exploits the heterogeneous and intransitive nature of peer networks to identify economically informative structural coefficients. In models of leverage levels, we detect significant peer-effect leverage coefficients that are on the order of 0.20, indicating a moderate but substantive level of strategic complementarity in capital structure decisions. We argue that prior…

首次公开募股的同伴效应AI 解读

This study investigates whether a private firm’s decision to go public affects the IPO decisions of its competitors. Using detailed data from the drug development industry, we identify a private firm’s direct competitors at a precise level through a novel approach using similarity in drug development projects based on disease targets. The analysis shows that a private firm is significantly more likely to go public after observing the recent IPO of a direct competitor, and…

含非对称性、长记忆与高阶矩溢出的已实现矩阵指数随机波动率模型AI 解读

The paper develops a novel realized matrix-exponential stochastic volatility model of multivariate returns and realized covariances that incorporates asymmetry and long memory (hereafter the RMESV-ALM model), and higher-moment spillovers. The matrix exponential transformation guarantees the positive definiteness of the dynamic covariance matrix. We decompose the likelihood function of the RMESV-ALM model into two components: one based on the conventional Kalman filter, and…

基于重抽样统计量的依赖稳健推断AI 解读

Summary We develop inference procedures robust to general forms of weak dependence. The procedures utilize test statistics constructed by resampling in a manner that does not depend on the unknown correlation structure of the data. We prove that the statistics are asymptotically normal under the weak requirement that the target parameter can be consistently estimated at the parametric rate. This holds for regular estimators under many well‐known forms of weak dependence and…

网络数据中协变量的降维AI 解读

Summary A problem of major interest in network data analysis is to explain the strength of connections using context information. To achieve this, we introduce a novel approach, called network-supervised dimension reduction, in which covariates are projected onto low-dimensional spaces to reveal the linkage pattern without assuming a model. We propose a new loss function for estimating the parameters in the resulting linear projection, based on the notion that closer…

一般干扰下因果效应随机化检验的图论方法AI 解读

Abstract Interference exists when a unit's outcome depends on another unit's treatment assignment. For example, intensive policing on one street could have a spillover effect on neighbouring streets. Classical randomization tests typically break down in this setting because many null hypotheses of interest are no longer sharp under interference. A promising alternative is to instead construct a conditional randomization test on a subset of units and assignments for which a…

推断型 Wasserstein 生成对抗网络AI 解读

Abstract Generative adversarial networks (GANs) have been impactful on many problems and applications but suffer from unstable training. The Wasserstein GAN (WGAN) leverages the Wasserstein distance to avoid the caveats in the minmax two-player training of GANs but has other defects such as mode collapse and lack of metric to detect the convergence. We introduce a novel inferential Wasserstein GAN (iWGAN) model, which is a principled framework to fuse autoencoders and WGANs.…

超图社群结构的检验AI 解读

Many complex networks in the real world can be formulated as hypergraphs where community detection has been widely used. However, the fundamental question of whether communities exist or not in an observed hypergraph remains unclear. This work aims to tackle this important problem. Specifically, we systematically study when a hypergraph with community structure can be successfully distinguished from its Erdős–Rényi counterpart, and propose concrete test statistics when the…

极端事件的空间依赖与时空趋势AI 解读

The statistical theory of extremes is extended to independent multivariate observations that are non-stationary both over time and across space. The non-stationarity over time and space is controlled via the scedasis (tail scale) in the marginal distributions. Spatial dependence stems from multivariate extreme value theory. We establish asymptotic theory for both the weighted sequential tail empirical process and the weighted tail quantile process based on all observations,…

广义张量估计的统计与计算最优框架AI 解读

This paper describes a flexible framework for generalized low-rank tensor estimation problems that includes many important instances arising from applications in computational imaging, genomics, and network analysis. The proposed estimator consists of finding a low-rank tensor fit to the data under generalized parametric models. To overcome the difficulty of nonconvexity in these problems, we introduce a unified approach of projected gradient descent that adapts to the…

气候政策的实体效应：融资约束与溢出AI 解读

We document that localized policies aimed at mitigating climate risk can have unintended consequences due to regulatory arbitrage by firms. Using a difference-in-differences framework to study the impact of the California cap-and-trade program with U.S. plant-level data, we show that financially constrained firms shift emissions and output from California to other states where they have similar plants that are underutilized. By contrast, unconstrained firms do not make such…

高维张量时间序列的因子模型AI 解读

Large tensor (multi-dimensional array) data routinely appear nowadays in a wide range of applications, due to modern data collection capabilities. Often such observations are taken over time, forming tensor time series. In this article we present a factor model approach to the analysis of high-dimensional dynamic tensor time series and multi-category dynamic transport networks. This article presents two estimation procedures along with their theoretical properties and…

动态网络上部分观测疫情的似然推断AI 解读

We propose a generative model and an inference scheme for epidemic processes on dynamic, adaptive contact networks. Network evolution is formulated as a link-Markovian process, which is then coupled to an individual-level stochastic susceptible-infectious-recovered model, to describe the interplay between the dynamics of the disease spread and the contact network underlying the epidemic. A Markov chain Monte Carlo framework is developed for likelihood-based inference from…