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因变量随机缺失时空间自回归模型的估计AI 解读

We suggest and compare different methods for estimating spatial autoregressive models with randomly missing data in the dependent variable. Aside from the traditional expectation-maximization (EM) algorithm, a nonlinear least squares method is suggested and a generalized method of moments estimation is developed for the model. A two-stage least squares estimation with imputation is proposed as well. We analytically compare these estimation methods and find that generalized…

线性及面板回归中空间误差相关的标准化LM检验AI 解读

The robustness of the Lagrange Multiplier (LM) tests for spatial error dependence of Burridge (1980) and Born and Breitung (2011) for the linear regression model, and Anselin (1988) and Debarsy and Etur (2010) for the panel regression model with random or fixed effects are examined. While all tests are asymptotically robust against distributional mis‐specification, their finite sample behaviour may be sensitive to the spatial layout. To overcome this shortcoming,…

重尾时间序列的矩条件检验AI 解读

We develop an asymptotically chi-squared statistic for testing moment conditions E[mt(θ0)]=0, where mt(θ0) may be weakly dependent, scalar components of mt(θ0) may have an infinite variance, and E[mt(θ)] need not exist for any θ under the alternative. Score tests are a natural application, and in general a variety of tests can be heavy-tail robustified by our method, including white noise, GARCH affects, omitted variables, distribution, functional form, causation, volatility…

空间自回归模型中准最大似然估计的有限样本偏误AI 解读

We investigate the finite-sample bias of the quasi-maximum likelihood estimator (QMLE) in spatial autoregressive models with possible exogenous regressors. We derive the approximate bias result of the QMLE in terms of model parameters and also the moments (up to order 4) of the error distribution, and thus a feasible bias-correction procedure is directly applicable. In some special cases, the analytical bias result can be significantly simplified. Our Monte Carlo results…

空间广义线性混合模型的降维与空间混杂缓解AI 解读

Summary Non-Gaussian spatial data are very common in many disciplines. For instance, count data are common in disease mapping, and binary data are common in ecology. When fitting spatial regressions for such data, one needs to account for dependence to ensure reliable inference for the regression coefficients. The spatial generalized linear mixed model offers a very popular and flexible approach to modelling such data, but this model suffers from two major shortcomings:…

空间 Probit 模型的部分最大似然估计AI 解读

This paper analyzes spatial Probit models for cross sectional dependent data in a binary choice context. Observations are divided by pairwise groups and bivariate normal distributions are specified within each group. Partial maximum likelihood estimators are introduced and they are shown to be consistent and asymptotically normal under some regularity conditions. Consistent covariance matrix estimators are also provided. Estimates of average partial effects can also be…

广义约化式拍卖：网络流方法AI 解读

We develop a network-flow approach for characterizing interim-allocation rules that can be implemented by ex post allocations. Our method can be used to characterize feasible interim allocations in general multi-unit auctions where agents face capacity constraints, both ceilings and floors. Applications include a variety of settings of practical interest, ranging from individual and group-specific capacity constraints, set-aside sale, partnership dissolution, and government…

识别技术溢出与产品市场竞争AI 解读

The impact of R&D on growth through spillovers has been a major topic of economic research over the last thirty years. A central problem in the literature is that firm performance is affected by two countervailing “spillovers” : a positive effect from technology (knowledge) spillovers and a negative business stealing effects from product market rivals. We develop a general framework incorporating these two types of spillovers and implement this model using measures of a…

给定图上的同时监督聚类与特征选择AI 解读

In this article, we propose a regression method for simultaneous supervised clustering and feature selection over a given undirected graph, where homogeneous groups or clusters are estimated as well as informative predictors, with each predictor corresponding to one node in the graph and a connecting path indicating a priori possible grouping among the corresponding predictors. The method seeks a parsimonious model with high predictive power through identifying and…

用混合整数规划匹配研究术后肾损伤AI 解读

This article presents a new method for optimal matching in observational studies based on mixed integer programming. Unlike widely used matching methods based on network algorithms, which attempt to achieve covariate balance by minimizing the total sum of distances between treated units and matched controls, this new method achieves covariate balance directly, either by minimizing both the total sum of distances and a weighted sum of specific measures of covariate imbalance,…

结构加性回归模型中用于函数选择的尖峰—厚尾先验AI 解读

Fabian Scheipla, Ludwig Fahrmeira & Thomas Kneibb a Department of Statistics , Ludwig-Maximilians-Universität München , Munich , Germany b Department of Economics , Georg-August-Universität Göttingen , Göttingen , Germany Structured additive regression (STAR) provides a general framework for complex Gaussian and non-Gaussian regression models, with predictors comprising arbitrary combinations of nonlinear functions and surfaces, spatial effects, varying coefficients, random…

空间自回归模型的相关结构AI 解读

This paper investigates how the correlations implied by a first-order simultaneous autoregressive (SAR(1)) process are affected by the weights matrix and the autocorrelation parameter. A graph theoretic representation of the covariances in terms of walks connecting the spatial units helps to clarify a number of correlation properties of the processes. In particular, we study some implications of row-standardizing the weights matrix, the dependence of the correlations on…

时空模型中的联合复合估计函数AI 解读

Summary Modelling of spatiotemporal processes has received considerable attention in recent statistical research. However, owing to the high dimensionality of the data, the joint modelling of spatial and temporal processes presents a great computational challenge, in both likelihood-based and Bayesian approaches. We propose a joint composite estimating function approach to estimating spatiotemporal covariance structures. This substantially reduces the computational…

信号博弈实验中的同伴学习AI 解读

SUMMARY We investigate peer group effects in laboratory experiments based on Milgrom and Roberts' (1982, Econometrica 50 : 443–459) entry limit pricing game. We generalize Heckman's (1981, in Structural Analysis of Discrete Data with Econometric Applications . MIT Press: Cambridge, MA) dynamic discrete‐choice panel data models by introducing time‐lagged social interactions, using the unbiased GHK simulator to implement the computationally cumbersome maximum likelihood…

利用面板数据估计溢出效应：课堂应用AI 解读

Obtaining consistent estimates of spillovers in an educational context is hampered by at least two issues: selection into peer groups and peer effects emanating from unobservable characteristics. We develop an algorithm for estimating spillovers using panel data that addresses both of these problems. The key innovation is to allow the spillover to operate through the fixed effects of a student's peers. The only data requirements are multiple outcomes per student and…

空间过程与近历依赖下的渐近推断AI 解读

The development of a general inferential theory for nonlinear models with cross-sectionally or spatially dependent data has been hampered by a lack of appropriate limit theorems. To facilitate a general asymptotic inference theory relevant to economic applications, this paper first extends the notion of near-epoch dependent (NED) processes used in the time series literature to random fields. The class of processes that is NED on, say, an α-mixing process, is shown to be…

国际市场联系与波动率传递AI 解读

This paper gauges volatility transmission between stock markets by testing conditional independence of their volatility measures. In particular, we check whether the conditional density of the volatility changes if we further condition on the volatility of another market. We employ nonparametric methods to estimate the conditional densities and model-free realized measures of volatility, allowing for both microstructure noise and jumps. We establish the asymptotic normality…

含变换噪声数据分类中的反卷积AI 解读

In the present study, we consider the problem of classifying spatial data distorted by a linear transformation or convolution and contaminated by additive random noise. In this setting, we show that classifier performance can be improved if we carefully invert the data before the classifier is applied. However, the inverse transformation is not constructed so as to recover the original signal, and in fact, we show that taking the latter approach is generally inadvisable. We…

面向遥感应用的空间统计数据融合AI 解读

Aerosols are tiny solid or liquid particles suspended in the atmosphere; examples of aerosols include windblown dust, sea salts, volcanic ash, smoke from wildfires, and pollution from factories. The global distribution of aerosols is a topic of great interest in climate studies since aerosols can either cool or warm the atmosphere depending on their location, type, and interaction with clouds. Aerosol concentrations are important input components of global climate models,…

随机块模型图的一致邻接谱嵌入AI 解读

We present a method to estimate block membership of nodes in a random graph generated by a stochastic blockmodel. We use an embedding procedure motivated by the random dot product graph model, a particular example of the latent position model. The embedding associates each node with a vector; these vectors are clustered via minimization of a square error criterion. We prove that this method is consistent for assigning nodes to blocks, as only a negligible number of nodes…

用于网络探索的多尺度社区块模型AI 解读

Real world networks exhibit a complex set of phenomena such as underlying hierarchical organization, multiscale interaction, and varying topologies of communities. Most existing methods do not adequately capture the intrinsic interplay among such phenomena. We propose a nonparametric Multiscale Community Blockmodel (MSCB) to model the generation of hierarchies in social communities, selective membership of actors to subsets of these communities, and the resultant networks…

细胞运动的贝叶斯时空动态建模：学习引导免疫反应的非线性趋性场AI 解读

Ioanna Manolopouloua, Melanie P. Matheub, Michael D. Cahalanb, Mike Westa & Thomas B. Keplerc a Department of Statistical Science , Duke University , Durham , NC , 27708 b Department of Physiology and Biophysics, School of Medicine , University of California , Irvine , CA , 92697 c Department of Microbiology , Boston University School of Medicine , Boston , MA , 02118 We develop and analyze models of the spatio-temporal organization of lymphocytes in the lymph nodes and…

基于局部独立图模型的反事实分析AI 解读

We show that one can perform causal inference in a natural way for continuous-time scenarios using tools from stochastic analysis. This provides new alternatives to the positivity condition for inverse probability weighting. The probability distribution that would govern the frequency of observations in the counterfactual scenario can be characterized in terms of a so-called martingale problem. The counterfactual and factual probability distributions may be related through a…

高维半参数高斯 Copula 图模型AI 解读

We propose a semiparametric approach called the nonparanormal SKEPTIC for efficiently and robustly estimating high-dimensional undirected graphical models. To achieve modeling flexibility, we consider the nonparanormal graphical models proposed by Liu, Lafferty and Wasserman [J. Mach. Learn. Res. 10 (2009) 2295–2328]. To achieve estimation robustness, we exploit nonparametric rank-based correlation coefficient estimators, including Spearman’s rho and Kendall’s tau. We prove…