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网络中的测量误差与同伴效应AI 解读

In many practical applications, only noisy proxies for the true regressors are available, which is commonly believed to induce an attenuation bias. In the linear-in-means model, however, estimated peer effects might be inflated, potentially leading to false positives. This paper shows that the asymptotic bias depends on the interplay between individual characteristics and network links and demonstrates how the network structure can facilitate identification without the need…

高维矩阵值时间序列的时空自回归模型AI 解读

Motivated by predicting intraday trading volume curves, we consider two spatio-temporal autoregressive models for matrix time series, in which each column may represent daily trading volume curve of one asset, and each row captures synchronized 5-minute volume intervals acrossmultiple assets. While traditional matrix time series focus mainly on temporal evolution, our approach incorporates both spatial and temporal dynamics, enabling simultaneous analysis of interactions…

具有动态异质溢出的矩阵值空间自回归AI 解读

We introduce a new time-varying parameter spatial matrix autoregressive model that integrates matrix-valued time series, heterogeneous spillover effects, outlier robustness, and time-varying parameters in one unified framework. The model allows for separate dynamic spatial spillover effects across both the row and column dimensions of the matrix-valued observations. Robustness can be introduced via a matrix Student’s t distribution. The model can still be estimated…

MOSAiC：多站点压缩风险函数的一次性聚合AI 解读

Multi-center research has strengthened the generalizability and reproducibility of scientific findings, yet multi-site inference in modern distributed research networks remains constrained by privacy regulations, heterogeneous site characteristics, and the logistical burden of human-in-the-loop, multi-round communication. In this work, we distill these operational constraints into four mathematical requirements for practical distributed inference and introduce MOSAiC, a…

通过模型平均实现多层网络迁移学习的框架AI 解读

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…

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离散空间随机场的有向图模型混合AI 解读

Current approaches for modeling discrete-valued outcomes associated with spatially-dependent areal units incur computational and theoretical challenges, especially in the Bayesian setting when full posterior inference is desired. As an alternative, we propose a novel statistical modeling framework for this data setting, namely a mixture of directed graphical models (MDGMs). The components of the mixture, directed graphical models, can be represented by directed acyclic…

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异质性部分干扰下观察性研究处理效应的半参数估计AI 解读

In many observational studies in social science and medicine, subjects or units are connected, and one unit’s treatment and attributes may affect another’s treatment and outcome, violating the stable unit treatment value assumption (SUTVA) and resulting in interference. To enable feasible estimation and inference, many previous works assume exchangeability of interfering units (neighbors). However, in many applications with distinctive units, interference is heterogeneous…

具有社群网络结构的高维多变量已实现波动率预测AI 解读

We introduce the Community Network Heterogeneous Autoregressive model with Quarticity (CNHARQ), a novel framework that integrates network-based information to enhance the forecasting of multivariate realized volatilities. To address the curse of dimensionality, we propose a Correlation-Based Stochastic Block Model (CBSBM) to uncover latent community structures from the correlation network of realized volatilities of N assets. This approach reduces the number of unknown…

利用解偏变量进行因果推断AI 解读

This paper shows that incorporating what we call antidotal variables (AV) into a causal treatment effects analysis can with one cross-sectional regression identify the causal effect, the spillover effect, as well as possible biases from selectivity. We apply the AV technique to analyze leave taking arising from the California Paid Family Leave (CPFL) program. Our analysis yields between a 55% and 70% larger treatment effect than the traditional DID methods, which we…

空间自回归模型的迁移学习：美国总统选举预测应用AI 解读

It is important to incorporate spatial geographic information into U.S. presidential election analysis, especially for swing states. The state-level analysis also faces significant challenges of limited spatial data availability. To address the challenges of spatial dependence and small sample sizes in predicting U.S. presidential election results using spatially dependent data, we propose a novel transfer learning framework within the SAR model, called as tranSAR. Classical…

SIMPLE-RC：非尖锐原假设与弱信号下的群组网络推断AI 解读

Large-scale network inference with uncertainty quantification has important applications in natural, social and medical sciences. The recent work of Fan et al. (J. R. Stat. Soc. Ser. B. Stat. Methodol. 84 (2022) 630–653) introduced a general framework of statistical inference on membership profiles in large networks (SIMPLE) for testing the sharp null hypothesis that a pair of given nodes share the same membership profile. In real applications, there are often groups of…

神经网络雅可比矩阵的谱渐近：收敛、普适性与相变AI 解读

We study the spectral properties of the Jacobian matrix in artificial neural networks and establish both its limiting spectral distribution and the second-order fluctuations of its spectrum. Our analysis uncovers fundamental phenomena, including universality properties with respect to the weight distribution and a phase transition in spectral behavior driven by the choice of activation function. Leveraging tools from random matrix theory, we first analyze a class of…

基于预测生成树方法的贝叶斯空间区域划分一致估计AI 解读

Bayesian model-based spatial clustering methods are widely used for their flexibility in estimating latent clusters with an unknown number of clusters while accounting for spatial proximity. Many existing methods are designed for clustering finite spatial units, limiting their ability to make predictions or may impose restrictive geometric constraints on the shapes of subregions. Furthermore, the posterior clustering consistency theory of spatial clustering models remains…

空间混杂下高斯随机场回归斜率的填充渐近一致可估性AI 解读

The problem of estimating the slope parameter in regression between two spatial processes under confounding by an unmeasured spatial process has received widespread attention in the recent statistical literature. Yet, a fundamental question remains unresolved: when is this slope consistently estimable under spatial confounding, with existing insights being largely empirical or estimator-specific. In this manuscript, we characterize conditions for consistent estimability of…

基于抛物型Monge–Ampère偏微分方程的无遗憾生成建模AI 解读

We introduce a novel generative modeling framework based on a discretized parabolic Monge–Ampère PDE, which emerges as a continuous limit of the Sinkhorn algorithm commonly used in optimal transport. Our method performs iterative refinement in the space of Brenier maps using a mirror gradient descent step. We establish theoretical guarantees for generative modeling through the lens of no-regret analysis, demonstrating that the iterates converge to the optimal Brenier map…

有向网络互惠效应的最优估计与检验AI 解读

The $p_1$ model plays a fundamental role in modeling directed networks, where the reciprocal effect parameter $ρ$ is of special interest in practice. However, due to nonlinear factors in this model, how to estimate $ρ$ efficiently is a long-standing open problem. We tackle the problem by the cycle count approach. The challenge is, due to the nonlinear factors in the model, for any given type of generalized cycles, the expected count is a complicated function of many…

似然模型中附带参数问题的 Neyman 正交化方法AI 解读

A popular approach to perform inference on a target parameter in the presence of nuisance parameters is to construct estimating equations that are orthogonal to the nuisance parameters, in the sense that their expected first derivative is zero. Such first-order orthogonalization allows the estimator of the nuisance parameters to converge at a slower-than-parametric rate. It may, however, not suffice when the nuisance parameters are very imprecisely estimated. Leading…

异质性主体下的极值理论AI 解读

Extreme value processes feature in any economic model in which agents receive a number of draws from some distribution and we examine the behavior of the maximum in the limit as the number of draws becomes large. This paper asks: Do the average outcomes of such processes change when different agents receive a different number of draws? To answer this, we allow the number of draws an agent receives from the underlying distribution (e.g. of productivities, ideas, or utility…

基于条件异方差识别日度油价冲击及其不确定性AI 解读

This paper develops a daily structural VAR for identifying oil price shocks from conditional heteroskedasticity and quantifies the transmission of their timevarying uncertainty. Identification exploits volatility clustering and spillovers in a structural GARCH specification and the complete identified set is derived. We derive covariance impulse response functions and introduce a time-varying forecast error variance decomposition (TV-FEVD) that allows for a decomposition of…

深度变换模型AI 解读

The increasing data complexity leads to the demand for more general and flexible modeling approaches. We consider a nonparametric transformation model with weak assumptions, which encompasses many popular models as special cases. A rank-based deep neural network (DNN) estimation approach is developed, with a loss function significantly different from the existing ones. It can accommodate outliers in response, which is a robustness property not broadly shared. For…

物理引导的统计数据融合：重建海洋涡旋三维流场AI 解读

Accurate reconstruction of three-dimensional ocean current fields is critical for understanding ocean dynamics and real-time conduct of modern oceanographic field campaigns, particularly for mesoscale eddy surveys. We propose a physics-guided modeling and learning framework for multi-source data fusion to estimate the three-dimensional (3D) current structure of oceanic eddies by integrating satellite altimetry and temperature data, ocean reanalysis data, and in situ drifting…

高维点过程的正则化谱估计AI 解读

Summary Advances in modern technology have enabled the simultaneous recording of neural spiking activity across large numbers of neurons, which statistically can be represented by a multivariate point process. We characterise the second order structure of this process via the spectral density matrix, a frequency domain equivalent of the covariance matrix. In the context of neuronal analysis, statistics based on the spectral density matrix can be used to infer connectivity in…

用分布回归双重差分评估最低工资引入对工资、收入与工时分布的影响AI 解读

ABSTRACT We estimate the causal effects of the introduction of the minimum wage in Germany on the distributions of hourly wages, monthly earnings, and hours worked, combining rich large‐scale data sources. We compare linear and non‐linear versions of distribution regression difference‐in‐differences (DR‐DiD) and also estimate bivariate DR‐DiD. We show that the introduction of the minimum wage in 2015 led to shifts in low hourly wages around the minimum wage threshold…

检测异质组织跨样本空间表达变异的灵活贝叶斯框架AI 解读

Spatial transcriptomics measures gene expression alongside the spatial coordinates of each capture spot or cell across tissue samples. The detection of spatially variable (SV) genes, whose expression exhibits systematic spatial variation, enables the delineation of functional tissue domains and the identification of region-specific transcriptional changes that underlie disease heterogeneity. However, empirical evidence from spatial transcriptomics data indicates that…