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因子数量的频带估计AI 解读

We introduce consistent estimators for the number of shocks driving large-dimensional dynamic factor models. Our estimator can be applied to single frequencies and specific frequency bands, making it suitable for disentangling shocks affecting dynamic models with a factor model representation. Noticeably, our estimator requires the time-series and cross-section size to diverge simultaneously without any constraint, and it is free of nuisance parameters, such as penalization…

借助辅助总体工具变量识别与多重稳健估计因果效应AI 解读

Estimating causal effects in a target population with unmeasured confounders is challenging, especially when instrumental variables (IVs) are unavailable. However, IVs from auxiliary populations with similar problems can help infer causal effects in the target population. While the homogeneous conditional average treatment effect assumption has been widely used for effect transportability, it has not been explored in IV-based data fusion. We include it as a basic approach,…

严格适当评分规则的局部化AI 解读

When comparing predictive distributions, forecasters are typically not equally interested in all regions of the outcome space. To address the demand for focused forecast evaluation, we propose a procedure to transform strictly proper scoring rules into their localized counterparts while preserving the score divergence and strict propriety. This is accomplished by applying the original scoring rule to a censored distribution. Our procedure nests the censored likelihood score…

种群规模依赖分枝过程的一致最小二乘估计AI 解读

We derive the first conditionally consistent estimators for a class of parametric Markov population models with logistic growth, which are suitable for modelling endangered populations in restricted habitats with a carrying capacity. We focus on discrete-time parametric population-size-dependent branching processes, for which we propose a new class of weighted least-squares estimators based on a single trajectory of population size counts. We establish the consistency and…

SPARCC：右删失协变量模型中的半参数稳健估计AI 解读

In Huntington disease research, a current goal is to understand how symptoms change prior to a clinical diagnosis. Statistically, achieving this goal entails modeling symptom severity as a function of the covariate 'time of diagnosis,' which is often heavily right-censored in observational studies. Existing estimators that handle right-censored covariates, such as the complete case estimator and maximum likelihood estimator, vary in their statistical efficiency and…

存在跨簇干扰的整群随机试验AI 解读

The literature on cluster-randomized trials typically allows for interference within but not across clusters. This may be implausible when units are irregularly distributed across space without well-separated communities, as clusters in such cases may not align with significant geographic, social, or economic divisions. This paper develops methods for reducing bias due to cross-cluster interference. We first propose an estimation strategy that excludes units not surrounded…

加料顺序实验的最优运行次序AI 解读

Order-of-addition experiments have emerged as a cornerstone in modern experimental design, yet the critical role of run-order has been entirely neglected in the literature. This oversight is surprising, given that the run order can significantly influence the cost, efficiency, and validity of the experiment. Certain run orders are inherently more economical and effective, while suboptimal orders may introduce unnecessary complexities or compromise results. To ensure…

高维广义线性混合模型的共轭梯度方法AI 解读

Generalized linear mixed models (GLMMs) are a widely used tool in statistical analysis. The main bottleneck of many computational approaches lies in the inversion of the high dimensional precision matrices associated with the random effects. Such matrices are typically sparse; however, the sparsity pattern resembles a multi partite random graph, which does not lend itself well to default sparse linear algebra techniques. Notably, we show that, for typical GLMMs, the Cholesky…

M 依赖随机场异常检测的扫描统计量及图像应用AI 解读

Anomaly detection in random fields is an important problem in many applications including the detection of cancerous cells in medicine, obstacles in autonomous driving and cracks in the construction material of buildings. Such anomalies are often visible as areas with different expected values compared to the background noise. Scan statistics based on local means have the potential to detect such local anomalies by enhancing relevant features. We derive limit theorems for a…

基于多样化投影的含潜在因子高维空间自回归AI 解读

We study one particular type of multivariate spatial autoregression (MSAR) model with diverging dimensions in both responses and covariates. This makes the usual MSAR models no longer applicable due to the high computational cost. To address this issue, we propose a factor-augmented spatial autoregression (FSAR) model. FSAR is a special case of MSAR but with a novel factor structure imposed on the high-dimensional random error vector. The latent factors of FSAR are assumed…

利用预测堆叠的贝叶斯地统计分析AI 解读

We develop Bayesian predictive stacking for geostatistical models, where the primary inferential objective is to provide inference on the latent spatial random field and conduct spatial predictions at arbitrary locations. We exploit analytically tractable posterior distributions for regression coefficients of predictors and the realizations of the spatial process conditional upon process parameters. We subsequently combine such inference by stacking these models across the…

一类对称性 Sobolev 检验的检测阈值与渐近功效AI 解读

We consider a broad class of symmetry hypothesis testing problems that includes the problems of testing uniformity or rotational symmetry on the hypersphere Sd−1, as well as the problem of testing sphericity in Rd. For this class, we study the null and non-null behaviors of Sobolev tests, with emphasis on their consistency rates and corresponding asymptotic powers. Our main results show that: (i) Sobolev tests exhibit a detection threshold that depends not only on the…

贝叶斯逆问题的时空 Besov 先验AI 解读

Fast development in science and technology has driven the need for proper statistical tools to capture special data features such as abrupt changes or sharp contrast. Many inverse problems in data science require spatiotemporal solutions derived from a sequence of time-dependent objects with these spatial features, for example, the dynamic reconstruction of computerized tomography (CT) images with edges. Conventional methods based on Gaussian processes (GP) often fall short…

小区域估计的实用度量：非对称均方预测误差AI 解读

The mean squared prediction error (MSPE) is widely used in small area estimation (SAE), as well as in other fields of statistics. Despite its popularity, the MSPE is not always practical in that it treats positive error, or over-prediction, and negative error, or under-prediction, equally. In practice, however, the consequences of these two types of errors are often different. This problem has long been known in statistics; however, a practical solution has not received much…

交互效应动态面板模型中 QMLE 的效率AI 解读

This paper studies the problem of efficient estimation of panel data models in the presence of an increasing number of incidental parameters. We formulate the dynamic panel as a simultaneous equations system, and derive the efficiency bound under the normality assumption. We then show that the Gaussian quasi-maximum likelihood estimator (QMLE) applied to the system achieves the efficiency bound without the normality assumption. Comparison of QMLE with the fixed effects…

重复半连续数据中长期趋势密度的非参数估计AI 解读

We consider nonparametric estimation of the density of the long-term trend of a semicontinuous variable observed repeatedly over time. These variables arise when measuring the intensity of an intermittent phenomenon, such as the intake of an episodically consumed nutrient or the concentration of an intermittent toxic substance: when the phenomenon is absent, the measurement is equal to zero; otherwise, it is positive. Semicontinuous data are usually represented by a two-part…

差分隐私切片逆回归：极小极大最优性与算法AI 解读

Privacy preservation has become a critical concern in high-dimensional data analysis due to the growing prevalence of data-driven applications. Since its proposal, sliced inverse regression has emerged as a widely used statistical technique to reduce the dimensionality of covariates while maintaining sufficient statistical information. In this paper, we propose optimally differentially private algorithms specifically designed to address privacy concerns in the context of…

面向分布福利的政策学习AI 解读

In this paper, we explore optimal treatment allocation policies that target distributional welfare. Most literature on treatment choice has considered utilitarian welfare based on the conditional average treatment effect (ATE). While average welfare is intuitive, it may yield undesirable allocations especially when individuals are heterogeneous (e.g., with outliers)—the very reason individualized treatments were introduced in the first place. This observation motivates us to…

短面板数据分析中的同质性识别新方法AI 解读

In panel data analysis, individual attributes are of importance in many real applications. With the advancement of data collection, it is often possible to acquire enough information for individual attributes in a collected panel dataset, and data from other individuals may contain the information for the attributes of the individual under concern. Homogeneity pursuit is an important topic in panel data analysis when individual attributes are of interest. Existing approaches…

基于卷积平滑支持向量机的去中心化网络高效分布式学习AI 解读

This article concerns efficiently classifying high-dimensional data over decentralized networks. Penalized support vector machines (SVMs) are widely used for high-dimensional classification tasks. However, the double nonsmoothness of the objective function poses significant challenges in developing efficient decentralized learning methods. Existing approaches frequently suffer from slow, sublinear convergence rates. To address this issue, we consider a convolution-based…

复杂时间动态下单调且平滑时变函数的同时推断AI 解读

We propose a new framework for the simultaneous inference of monotone and smoothly time-varying functions under complex temporal dynamics. This will be done utilizing the monotone rearrangement and the nonparametric estimation. We capitalize the Gaussian approximation for the nonparametric monotone estimator and construct the asymptotically correct simultaneous confidence bands (SCBs) using designed bootstrap methods. We investigate two general and practical scenarios. The…

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通过嵌入对齐实现链式多矩阵整合AI 解读

Motivated by the increasing demand for multi-source data integration in various scientific fields, in this article we study matrix completion in scenarios where the data exhibits certain block-wise missing structures—specifically, where only a few noisy submatrices representing (overlapping) parts of the full matrix are available. We propose the Chain-linked Multiple Matrix Integration (CMMI) procedure to efficiently combine the information that can be extracted from these…

大规模低秩回归模型下的迁移学习AI 解读

In high-dimensional multiple response regression problems, the large dimensionality of the coefficient matrix poses a challenge to parameter estimation. To address this challenge, low-rank matrix estimation methods have been developed to facilitate parameter estimation in the high-dimensional regime, where the number of parameters increases with sample size. Despite these methodological advances, accurately predicting multiple responses with limited target data remains a…

可解释的市场微观结构噪声AI 解读

High-frequency financial data are often contaminated by market microstructure effects. In this study, we consider a setting where a portion of the microstructure noise can be explained by observable trading information, referred to as the explicative noise component. To formally analyze this component, we first develop a model-free variable importance measure in the high-frequency setting that quantifies the price impact of subsets of trading variables. Based on the…