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利用端元变异性进行高效光谱解混AI 解读

A mixed signal vector composed of a weighted sum of random, unobserved endmember vectors has a mean and a variance that both depend on the unknown weights. This suggests that linear estimates of the weights based on the mean model are likely to be inefficient, and could possibly be improved upon by estimates that also make use of the variance model. In the context of a latent factor model for source variability, we calculate theoretically how much information is available in…

非平衡面板固定效应估计量的推断AI 解读

We develop the asymptotic theory for two-way fixed effects M-estimators in unbalanced panels, within a framework where both panel dimensions grow large at proportional rates. The selection process may be deterministic, stochastic, or a combination of the two. We require neither a missing-at-random condition nor a selection equation, only a conditional mean restriction on the outcome. The uncorrected estimators are asymptotically normal but not correctly centered due to…

三维面板与网络数据固定效应估计的去偏推断AI 解读

Inference for fixed effects estimators is often unreliable due to Nickell- and incidental parameter biases. While these issues are well understood for classical two-dimensional panels, little is known about three-dimensional panel structures (e.g., sender x receiver x time). We develop inferential theory for a broad class of linear and nonlinear fixed effects M-estimators in this setting, covering bipartite, directed, and undirected network panel data, multiple…

纵向处理效应的贝叶斯模型平均估计AI 解读

Several methods have been proposed for performing variable selection for the goal of estimating the average treatment effect of a point treatment on an outcome. Much less work has focused on developing methods for performing variable selection when the goal is instead to estimate the causal effect of a longitudinal treatment on an outcome. We introduce a Bayesian model averaging approach to estimate counterfactual means under longitudinal treatment regimes as well as the…

模型误设模拟器下的探索与实验AI 解读

Randomized experiments are the standard for measuring causal effects that guide decisions, but are costly. Simulators calibrated to historical data offer a cheaper alternative, but their policies can become suboptimal under drift, a change in dynamics after calibration. We study if, when, and how to collect data for recalibrating the simulator. We consider a system with multiple units running in parallel and managed by a single operator. The real-world system is a partially…

超越条件独立的因果发现：有向无环与循环图的可识别性、算法与统计推断AI 解读

Learning causal structure from data requires assumptions about mechanisms, noises, observation schemes, and interventions. Historically, causal discovery has relied primarily on conditional independence. This review focuses on modern causal discovery methods that exploit properties of distributions beyond conditional independence. Our organizing principle separates the source of identification from the statistical estimator and its computational implementation. This…

密集相邻变点检测及其在地缘政治冲击中的应用AI 解读

Change points that arrive in quick succession, or near the boundaries of the sample, are prone to type-II error: the few observations trapped between successive breaks carry too little information for global procedures to analyze. This article develops a test statistic, $I_1$, for which we derive the limiting null distribution under Gaussian noise, extend the theory to weakly dependent sequences, and prove consistency under a mean-shift alternative. Around $I_1$, we…

函数型结局下的处理效应异质性检验AI 解读

Assessing treatment-effect heterogeneity is important in causal inference, particularly for functional outcomes where effects may vary over time and the common effect is unknown. We propose a nonparametric procedure that assesses treatment-effect homogeneity through a testable marginal implication. By comparing within-arm difference distributions, the construction removes the unknown common functional effect without estimating it. We measure departures between these…

在线条件藤Copula：电力需求预测AI 解读

We propose an online multivariate distributional forecasting model in which we condition the parameters of a vine copula on covariates. An online algorithm updates the parameters sequentially using only the most recent observations. Existing approaches require full batch re-estimation using all available data at each update step, which makes them computationally slow in large-scale forecasting applications and less suitable for settings in which dependence structures change…

A large literature estimates child penalties using event studies by gender, normalizing by predicted earnings absent children and reporting the gender gap. I formalize the design's identification assumption: normalized parallel trend violations are equal across genders, which I term normalized triple differences (NTD). Under NTD, the conventional target, the gender gap in normalized effects, is not identified when parallel trends fails. However, the effect of parenthood on…

共形策略选择：何时部署AI决策以实施定向干预AI 解读

Artificial-intelligence (AI) policies are increasingly used to make consequential decisions in medicine, finance, education, and public services, yet deploying them responsibly requires knowing not only whether a policy helps on average, but for whom. Existing tools for off-policy evaluation and safe policy learning provide population-level guarantees, but some individuals may experience adverse treatment effects. We recast targeted deployment as a multiple-testing problem:…

联合图模型的学习：计算效率、动态正则化与自适应性AI 解读

Multi-sourced datasets are common in studies of variable interactions, for example, heterogeneous gene networks, cross-domain recommendation, etc., where each source induces a related but distinct dependency structure. Joint learning of multiple graphical models (i.e., multiple precision matrices) has emerged as an important tool in analyzing such data. Unlike separate learning, joint learning can leverage shared structural patterns across graphs to yield more accurate…

The autocovariance function (ACF) is central to the analysis of time series, spatial, and spatio-temporal data. Classical parametric models are often too restrictive for multivariate and space-time data, which motivates non-parametric descriptions of the ACF. Bochner's Theorem guarantees that any positive spectral measure yields a valid ACF via the inverse Fourier transform; however, existing non-parametric approaches in the spectral domain rarely return closed-form…

非单调缺失数据的模式图模型及其在心肌梗死并发症中的应用AI 解读

Non-monotone missingness is frequently encountered in practice, with intermittent dropout and re-entry in longitudinal studies as a canonical example. Under non-monotone missingness, the missing at random (MAR) assumption often lacks a clear causal interpretation and imposes implicit constraints on the joint distribution that may be difficult to justify. Within the pattern graph framework, we propose a fully parametric estimation procedure that combines…

RBF 核高斯过程最大似然估计的精确渐近理论AI 解读

Gaussian processes (GPs) are widely used across machine learning, spatial statistics, time-series analysis, optimization, Bayesian statistics, and scientific applications. A central component of a GP model is its kernel, which is typically specified through a parametric family. Among the most widely used choices is the radial basis function (RBF), also known as the squared exponential or Gaussian kernel, owing to its simple form, smoothness, and flexibility. In practice, the…

Panel data models with unobserved heterogeneity in the form of interactive effects standardly assume that the time effects -- or ``common factors'' -- enter linearly. This assumption is restrictive because it concerns an unobserved component of the model, for which a particular functional form is rarely justified. By contrast, linearity in the observable regressors can often be motivated by economic theory or empirical convention. Linearity in the factors has persisted…

基于Singh-Maddala排放分布的贝叶斯隐马尔可夫重尾状态转换时间序列模型AI 解读

Heavy-tailed time series with regime switching arise in environmental, financial, and engineering applications, yet conventional emission distributions such as Gaussian, Gamma, or Weibull fail to capture simultaneously extreme right-skewness, slowly decaying upper tails, and temporal dependence. This paper develops a fully Bayesian Hidden Markov Model with Singh-Maddala (Burr Type XII) emission distributions (HMMSM). We derive the complete Bayesian structure and design an…

基于单人移动设备时间序列的近端因果推断AI 解读

Unmeasured confounding challenges causal inference in intensive longitudinal studies, potentially biasing treatment effect estimates. The proximal causal inference (PCI) framework offers a promising approach to identification using proxies or negative control variables in the presence of hidden confounding bias. While prior literature considers the joint effect of time-varying treatments, our work extends the framework to a time series setting to estimate the contemporaneous…

We propose SLIM (Stochastic Learning and Inference in overidentified Models), a new scalable stochastic approximation algorithm for nonlinear GMMs with many nonseparable moments and parameters. SLIM is shown to be consistent and asymptotically normal without a consistent initial estimator or a globally convex GMM criterion. Random-scaling and plug-in based confidence set constructions remain valid when computational uncertainty can be non-negligible relative to sampling…

边际敏感性模型下的锐利工具变量界AI 解读

Marginal sensitivity models bound the degree of unmeasured confounding but do not incorporate instrumental-variable restrictions, even when an instrument is available. We develop a framework combining these models with instrumental-variable assumptions to sharpen inference on population treatment effects without requiring treatment monotonicity or treatment-effect homogeneity. For continuous and finite ordered outcomes, we characterize sharp identified sets for…

Uniform confidence bands are widely used in empirical analysis for uncertainty qualification of nonparametric inference of unknown functions. A variety of simple implementation methods, including multiplier bootstrap, have been proposed and theoretically justified. However, an implementation over a literally continuous index set is generally computationally infeasible, and practitioners therefore compute the critical value by evaluating the statistic on a finite evaluation…

Switchback experiments are widely used in pricing applications. We consider a retailer running such an experiment for the price of a single product, with infinite supply. In each period, the seller chooses a price from a set of predefined prices consisting of an original price and a few discounted price levels. The goal is to estimate the demand gradient at the original price to adjust the original price and improve revenue after the experiment. In our model, in each period,…

In a statistical factor model, principal components (or eigenvectors) of a sample covariance matrix serve as estimates of principal directions, the true drivers of co-movement of a collection of observed variables. We write the often substantial error in these estimates as a sum of two interpretable terms, which we show have almost sure asymptotic limits as the number of variables grows with sample size bounded. This scenario is commonplace in financial economics, genomics,…

先筛选再子抽样：面向相关协变量大规模生物医学数据的高效模型训练AI 解读

Statistical modeling of high-dimensional and large-sample data with complex correlation structures is increasingly important in biomedical research, particularly in large-scale omics applications such as genomic studies. Analyzing these large-scale datasets imposes substantial computational and memory demands, requiring efficient model training strategies that maintain predictive accuracy. We propose Prescreen, Then Subsample (PTS), a two-stage approach that combines…