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含非参数内生效应的空间自回归面板模型估计AI 解读

This paper proposes a sieve generalized method of moments (GMM) method for the estimation of spatial autoregressive panel data models with nonparametric endogenous effect. The new estimator incorporates both linear moments based on the orthogonality of the exogenous regressors with the model disturbances and quadratic moments based on the properties of idiosyncratic errors. We establish the consistency and asymptotic normality of the sieve GMM estimator and show that it is…

分位数图模型：预测、条件独立与系统性风险应用AI 解读

We propose two types of Quantile Graphical Models: (i) Conditional Independence Quantile Graphical Models (CIQGMs) characterize the conditional independence by evaluating the distributional dependence structure at each quantile index, as such, those can be used for validation of the graph structure in the causal graphical models; (ii) Prediction Quantile Graphical Models (PQGMs) characterize the statistical dependencies through the graphs of the best linear predictors under…

社会溢出下隔离效应的非参数测量AI 解读

In this paper we nonparametrically analyze the effects of reallocating individuals across social groups in the presence of social spillovers. Individuals are either 'high' or 'low' types. Own outcomes may vary with the fraction of high types in one's social group. We characterize the average outcome and inequality effects of small increases in segregation by type. We also provide a measure of average spillover strength. We generalize the setup used by Benabou (1996) and…

双边关系模型中遗漏异质性的检验AI 解读

We develop a Lagrange Multiplier (LM) test of neglected heterogeneity in dyadic models. The test statistic is derived by modifying Breusch and Pagan (1980)’s test. We establish the asymptotic distribution of the test statistic under the null using a novel martingale construction. We also consider the power of the LM test in generic panel models. Even though the test is motivated by random effects, we show that it has a power for detecting fixed effects as well. Finally, we…

融资约束与种族住房差距AI 解读

We show that financial constraints lead to spatial misallocation and contribute to racial disparities in housing and wealth accumulation. Using bunching and difference-in-differences designs, we document that down payment constraints disproportionately limit the ability of Black households to access housing in high-opportunity areas. We build a dynamic life-cycle model to examine the long-term wealth effects of these leverage distortions on group differences in wealth…

利用部分网络数据估计同伴效应AI 解读

Abstract We study the estimation of peer effects through social networks when researchers do not observe the entire network structure. Special cases include sampled networks, censored networks, and misclassified links. We assume that researchers can obtain a consistent estimator of the distribution of the network. We show that this assumption is sufficient for estimating peer effects using a linear-in-means model. We provide an empirical application to the study of peer…

网络关联数据的高维因子分析AI 解读

Summary Factor analysis is a statistical tool widely used in many disciplines, such as psychology, economics and sociology. As observations linked by networks become increasingly common, incorporating network structures into factor analysis is an important problem that remains open. This article focuses on high-dimensional factor analysis involving network-connected observations, and we propose a generalized factor model with latent factors that account for both the network…

精确平衡的有限总体样本：存在性与应用AI 解读

Abstract Samples selected from finite populations can rarely be exactly balanced, as sample selection is an integer problem and the balancing equations are strict equalities. Selecting a balanced sample is not a problem limited to survey sampling. It also applies to design of experiments, clinical trials, causality, exact inference, graph theory and network analysis. Building on Jean-Claude Deville’s foundational work, we explore conditions under which exact solutions are…

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,…

非欧随机对象的稳健函数主成分分析AI 解读

Summary Functional data analysis offers a diverse toolkit of statistical methods tailored to analysing samples of real-valued random functions. Recently, samples of time-varying random objects, such as time-varying networks, have been increasingly encountered in data analysis. These data structures represent elements within general metric spaces that lack local or global linear structures, rendering traditional functional data analysis methods inapplicable. Moreover, the…

利用统计推断增强 AI 生成生物医学图像的可信度AI 解读

Generative artificial intelligence (AI) has transformed the biomedical imaging field through image synthesis, addressing challenges of data availability, privacy, and diversity in biomedical research. This paper proposes a novel nonparametric method within the functional data framework to discern significant differences between the mean and covariance functions of original and synthetic biomedical imaging data, thereby enhancing the fidelity and utility of synthetic data.…

受访者驱动抽样中个体网络度数的潜变量模型AI 解读

Respondent-driven sampling (RDS) is widely used to collect data from hidden populations in social and biomedical science. Although RDS may provide comprehensive coverage of the target hidden population through social network recruitment, its non-random sampling process poses challenges for generalizing findings beyond the sample. Current analytical methods rely on the network size (degree) reported by respondents to adjust for unequal sampling probabilities. However, the…

通过 GLASS 进行脑机接口的贝叶斯推断AI 解读

Brain-computer interfaces (BCIs), particularly the P300 BCI, facilitate direct communication between the brain and computers. The fundamental statistical problem in P300 BCIs lies in classifying target and non-target stimuli based on electroencephalogram (EEG) signals. However, the low signal-to-noise ratio (SNR) and complex spatial/temporal correlations of EEG signals present challenges in modeling and computation, especially for individuals with severe physical…

基于交叉验证与一般损失函数的统一最优模型平均AI 解读

Studying unified model averaging estimation for situations with complicated data structures, we propose a novel model averaging method based on cross-validation ( MACV). MACV unifies a large class of new and existing model averaging estimators and covers a very general class of loss functions. Furthermore, to reduce the computational burden caused by the conventional leave-subject/one-out cross validation, we propose a SEcond-order-Approximated Leave-one/subject-out ( SEAL)…

维度递增随机图模型中的似然比检验AI 解读

We explore the Wilks phenomena in two random graph models: the β-model and the Bradley–Terry model. For two increasing dimensional null hypotheses, including a specified null H0:βi=βi0 for i=1,…,r and a homogenous null H0:β1=⋯=βr, we reveal high dimensional Wilks’ phenomena that the normalized log-likelihood ratio statistic, [2{l(β̂)−l(β̂0)}−r]/(2r)1/2, converges in distribution to the standard normal distribution as r goes to infinity. Here, l(β) is the log-likelihood…

基于网络的邻域回归AI 解读

Given the ubiquity of modularity in biological systems, module-level regulation analysis is vital for understanding biological systems across various levels and their dynamics. Current statistical analysis on biological modules predominantly focuses on either detecting the functional modules in biological networks or sub-group regression on the biological features without using the network data. This paper proposes a novel network-based neighborhood regression framework…

网络变系数模型AI 解读

We propose a novel network-varying coefficient model that extends traditional varying coefficient models to accommodate network data. The main idea is to model the regression coefficients as the functions of the latent “locations” of network nodes that drive formation of the network. To estimate the model, we identify the latent “locations” via the latent space model and then develop an iterative projected gradient descent algorithm by optimizing the network parameters and…

高斯图模型中的枢纽节点检测AI 解读

Graphical models are popular tools for exploring relationships among a set of variables. The Gaussian graphical model (GGM) is an important class of graphical models, where the conditional dependence among variables is represented by nodes and edges in a graph. In many real applications, we are interested in detecting hubs in graphical models, which refer to nodes with a significant higher degree of connectivity compared to non-hub nodes. A typical strategy for hub detection…

大型向量自回归模型中的网络格兰杰因果关系发现AI 解读

This article proposes novel inferential procedures for discovering the network Granger causality in highdimensional vector autoregressive models.In particular, we mainly offer two multiple testing procedures designed to control the false discovery rate (FDR).The first procedure is based on the limiting normal distribution of the t-statistics with the debiased lasso estimator.The second procedure is its bootstrap version.We also provide a robustification of the first…

度量空间上张量积再生核希尔伯特空间的方差分析AI 解读

Many methods have been developed to analyze complex data, such as non-Euclidean shape, network, and manifold data. However, there is a lack of methods for studying interactions among complex data. In this article, we first propose a novel kernel function for a metric space and construct its associated reproducing kernel Hilbert space. The new nonstationary kernel function provides a flexible and powerful tool for learning complex structures in non-Euclidean data. We then…

利用广义变系数模型检测不规则区域中的局部信号AI 解读

In spatial analysis, it is essential to understand and quantify spatial or temporal heterogeneity. This paper focuses on the generalized spatially varying coefficient model (GSVCM), a powerful framework to accommodate spatial heterogeneity by allowing regression coefficients to vary in a given spatial domain. We propose a penalized bivariate spline method for detecting local signals in GSVCM. The key idea is to use bivariate splines defined on triangulation to approximate…

广义自回归条件贝塔：多因子资产定价中的纵向反馈AI 解读

We propose a new class of observation-driven models, Generalized Autoregressive Conditional Betas, which describe the joint dynamics of time-varying slopes in a system of conditionally heteroskedastic simultaneous multiple regressions. The model accommodates large dimensions, parametric longitudinal restrictions, exogenous variables, and the coexistence of constant and time-varying slopes. It also introduces new mechanisms for the transmission of shocks, namely, beta…

因子网络自回归AI 解读

We propose a factor network autoregressive (FNAR) model for time series with complex network structures. The coefficients of the model reflect many different types of connections between economic agents (“multilayer network”), which are summarized into a smaller number of network matrices (“network factors”) through a novel tensor-based principal component approach. We provide consistency and asymptotic normality results for the estimation of the factors, their loadings, and…

时空点过程的部分线性单指标模型与函数主成分分析AI 解读

We model spatially and temporally indexed point process data as a multi-level log-Gaussian Cox process where the log intensity function depends on a partially linear single-index structure of spatio-temporal covariates and three latent functional random effects representing the spatial and temporal random effects as well as their interactions. We assume that the latent functional effects are Gaussian processes with Karhunen-Loève representations, and model the unknown link…