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任意元素依赖下的结构化矩阵学习与马尔可夫转移核估计AI 解读

The problem of structured matrix estimation has been studied mostly under strong noise dependence assumptions. This paper considers a general framework of noisy low-rank-plus-sparse matrix recovery, where the noise matrix may come from any joint distribution with arbitrary dependence across entries. We propose an incoherent-constrained least-square estimator and prove its tightness both in the sense of deterministic lower bound and matching minimax risks under various noise…

连锁不平衡评分回归及跨祖源扩展的高维统计推断AI 解读

Linkage disequilibrium score regression (LDSC) has emerged as an essential tool for genetic and genomic analyses of complex traits, utilizing high-dimensional data derived from genome-wide association studies (GWAS). LDSC computes the linkage disequilibrium (LD) scores using an external reference panel, and integrates the LD scores with only summary data from the original GWAS. In this paper, we investigate LDSC within a fixed-effect data integration framework, underscoring…

深层马蹄高斯过程AI 解读

Deep Gaussian processes have recently been proposed as natural objects to fit, similarly to deep neural networks, possibly complex features present in modern data samples, such as compositional structures. Adopting a Bayesian nonparametric approach, it is natural to use deep Gaussian processes as prior distributions, and use the corresponding posterior distributions for statistical inference. We introduce the deep Horseshoe Gaussian process Deep–HGP, a new simple prior based…

函数型图形套索AI 解读

We consider the problem of recovering conditional independence relationships between p jointly distributed Hilbertian random elements given n realizations thereof. We operate in the sparse high-dimensional regime, where n ≪ p and no element is related to more than d ≪ p other elements. In this context, we propose an infinite-dimensional generalization of the graphical lasso. We prove model selection consistency under natural assumptions and extend many classical results to…

协变量依赖转移概率的隐马尔可夫状态下，混合模型的稳健性AI 解读

This article studies the robustness of quasi-maximum-likelihood estimation in hidden Markov models when the regime-switching structure is misspecified. Specifically, we examine the case where the data-generating process features a hidden Markov regime sequence with covariate-dependent transition probabilities, but estimation proceeds under a simplified mixture model that assumes regimes are independent and identically distributed. We show that the parameters governing the…

从模型选择到模型平均：嵌套线性模型的比较AI 解读

Model selection (MS) and model averaging (MA) are two popular approaches when many candidate models exist. Theoretically, the estimation risk of an oracle MA is not larger than that of an oracle MS because the former is more flexible, but a foundational issue is this: Does MA offer a substantial improvement over MS? Recently, seminal work by Peng and Yang (2022) has answered this question under nested models with linear orthonormal series expansion. In the current paper, we…

重复测量反卷积估计量的更快一致收敛率AI 解读

Recently, Kurisu and Otsu (2022b, Econometric Theory 38(1), 172–193) derived the uniform convergence rates for the nonparametric deconvolution estimators proposed by Li and Vuong (1998, Journal of Multivariate Analysis 65(2), 139–165). This article shows that faster uniform convergence rates can be established for their estimators under the same assumptions. In addition, a new class of deconvolution estimators based on a variant of Kotlarski’s identity is also proposed. It…

函数依赖在空间计量经济学中的应用AI 解读

In this paper, we generalize the concept of functional dependence (FD) from time series (see Wu [2005, Proceedings of the National Academy of Sciences 102, 14150–14154]) and stationary random fields (see El Machkouri, Volný, and Wu [2013, Stochastic Processes and Their Applications 123, 1–14]) to nonstationary spatial processes. Within conventional settings in spatial econometrics, we define the concept of spatial FD measure and establish a moment inequality, an exponential…

空间自回归模型中的异方差稳健设定检验AI 解读

Spatial autoregressive (SAR) and related models offer flexible yet parsimonious ways to model spatial and network interactions. SAR specifications typically rely on a particular parametric functional form and an exogenous choice of the so-called spatial weight matrix with only limited guidance from theory in making these specifications. Also, the choice of a SAR model over other alternatives, such as spatial Durbin (SD) or spatial lagged X (SLX) models, is often arbitrary,…

需求的实解析离散选择模型：理论与含义AI 解读

We demonstrate that a large class of discrete choice models of demand can be approximated by real analytic demand models. We obtain this result by combining (i) a novel real analytic property of the mixed logit and the mixed probit models with any distribution of random coefficients and (ii) an approximation property of finite mixtures of Gumbel and Gaussian distributions. To illustrate some of the implications of this result, we discuss how real analyticity facilitates…

有限重叠的检验AI 解读

Extreme propensity scores arise in observational studies when treated and control units have very different characteristics. This is commonly referred to as limited overlap . In this paper, we propose a formal statistical test that helps assess the degree of limited overlap. Rejecting the null hypothesis in our test indicates either no or very mild degree of limited overlap and hence reassures that standard treatment effect estimators will be well behaved. One distinguishing…

空间相关数据模型的Moran’s I lasso估计AI 解读

Summary This paper proposes a lasso-based estimator which uses information embedded in the Moran statistic to develop a selection procedure called Moran’s I lasso (Mi-lasso) to solve the eigenvector spatial filtering (ESF) eigenvector selection problem. ESF uses a subset of eigenvectors from a spatial weights matrix to efficiently account for any omitted spatially correlated terms in a classical linear regression framework, thus eliminating the need for the researcher to…

对序列依赖稳健的横截面相关自助法检验AI 解读

Summary We propose a novel bootstrap-based test for cross-sectional dependence (CD) in panel models, maintaining robustness against serial dependence. While serial dependence is common in panel data, existing tests often assume serial independence. Our cluster wild bootstrap CD test procedure mirrors Pesaran’s original CD test and is very simple to implement. This procedure preserves serial dependence while testing for cross-sectional independence. Theoretical validity is…

半参数模型中的交叉拟合经验似然AI 解读

Summary We propose a new cross-fitted empirical likelihood (CFEL) inference procedure for low-dimensional parameters in the presence of complex, infinite dimensional nuisance parameters that may be estimated by modern machine-learning methods. Relying on the Neyman orthogonal moment condition and cross-fitting as a sample-splitting procedure, we show that our CFEL statistic is asymptotically pivotal under weak conditions often invoked in Wald-type approaches. Three examples…

说服研究中的二元工具变量模型：刻画依从者的说服类型AI 解读

Summary In an empirical study of persuasion, researchers often use a binary instrument to encourage individuals to consume information and take some action. We show that, with a binary Imbens–Angrist instrumental variable model and the monotone treatment response assumption, it is possible to identify the joint distribution of potential outcomes among compliers. This is necessary to identify the percentage of mobilized voters and their statistical characteristic defined by…

面向大型数据集的双样本量估计量AI 解读

Summary In generalized method of moments (GMM) estimators, moment conditions with additive error terms involve an observed component and a predicted component. If the predicted component is computationally costly to evaluate, it may not be feasible to estimate the model with all the available data. We propose a simple two-sample ‘large–small’ size estimator that uses the full dataset for the computationally cheap observed component, but a reduced sample size for the…

使Lee界更稳健且更紧：随机单调性与对称性下的样本选择模型AI 解读

Summary In the presence of sample selection, Lee’s (2009, Review of Economic Studies 76, 1071–102) non-parametric bounds are a popular tool for estimating a treatment effect. However, the Lee bounds rely on the monotonicity assumption, the empirical validity of which is sometimes unclear. Furthermore, the bounds are often regarded to be wide and less informative even under monotonicity. To address these issues, this study introduces a stochastic version of the monotonicity…

非参数条件前沿与效率估计：一种新方法AI 解读

Summary In production theory, conditional frontiers and conditional efficiency measures are flexible and appealing tools to investigate the role of environmental variables in the production process. Direct approaches estimate non-parametrically conditional distribution functions requiring smoothing techniques and the use of bandwidths. Traditional methods for selecting bandwidths provide bandwidths of orders that may not be optimal for estimating the boundary of the…

通过序贯蒙特卡洛实现无偏且一致的嵌套采样AI 解读

Abstract We introduce a new class of sequential Monte Carlo methods which reformulates the essence of the nested sampling (NS) method of Skilling in terms of sequential Monte Carlo techniques. Two new algorithms are proposed: nested sampling via sequential Monte Carlo (NS-SMC) and adaptive nested sampling via sequential Monte Carlo (ANS-SMC). The new framework allows convergence results to be obtained in the setting when Markov chain Monte Carlo (MCMC) is used to produce new…

聚焦对象的置信保障：具有选择条件覆盖率的保形预测AI 解读

Abstract Conformal prediction builds marginally valid prediction intervals that cover the unknown outcome of a randomly drawn test point with a prescribed probability. However, in practice, data-driven methods are often used to identify specific test unit(s) of interest, requiring uncertainty quantification tailored to these focal units. In such cases, marginally valid conformal prediction intervals may fail to provide valid coverage for the focal unit(s) due to selection…

海量数据线性分类的多分辨率子抽样AI 解读

Abstract Subsampling is one of the popular methods to balance statistical efficiency and computational efficiency in the big data era. Most approaches aim to select informative or representative sample points to achieve good overall information of the full data. The present work takes the view that sampling techniques are recommended for the region we focus on and summary measures are enough to collect the information for the rest according to a well-designed data…

凸性与统计关联度量AI 解读

Abstract Recent investigations on the measures of statistical association highlight essential properties such as zero-independence (the measure is zero if and only if the random variables are independent), monotonicity under information refinement, and max-functionality (the measure of association is maximal if and only if we are in the presence of a deterministic (noiseless) dependence). An open question concerns the reasons why measures of statistical associations satisfy…

通过下注构造序贯蒙特卡洛检验AI 解读

Abstract In a Monte Carlo test, the observed dataset is fixed, and several resampled or permuted versions of the dataset are generated in order to test a null hypothesis that the original dataset is exchangeable with the resampled/permuted ones. Sequential Monte Carlo tests aim to save computational resources by generating these additional datasets sequentially one by one and potentially stopping early. While earlier tests yield valid inference at a particular prespecified…

模块化贝叶斯推断中切断反馈的一般框架AI 解读

Abstract Standard Bayesian inference enables building models that combine information from various sources, but this inference may not be reliable if components of the model are misspecified. Cut inference, a particular type of modularized Bayesian inference, is an alternative that splits a model into modules and cuts the feedback from any suspect module. Previous studies have focused on a two module case, but a more general definition of a ‘module’ remains unclear. We…