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意大利北部PM10评估的三种分布方法AI 解读

We propose three spatial methods for estimating the full probability distribution of PM10 concentrations, with the ultimate goal of assessing air quality in Northern Italy. Moving beyond spatial averages and simple indicators, we adopt a distributional perspective to capture the complex variability of pollutant concentrations across space. The first proposed approach predicts class-based compositions via Fixed Rank Kriging; the second estimates multiple, non-crossing…

多中介情形下自然直接与间接相对风险的回归方法AI 解读

Mediation analysis investigates whether part of the treatment effect is channelled through one or more mediators along the causal pathway between the treatment and the primary outcome. However, the presence of multiple, potentially dependent mediators raises substantial challenges, particularly when the outcome is binary, the mediators are measured on different scales, and interactions are present. We consider on causal mediation analysis with a binary treatment and a binary…

单人试验的因果推断AI 解读

The aim of personalized medicine is to tailor treatment decisions to individuals' characteristics. N-of-1 trials are within-person crossover trials that hold the promise of targeting individual-specific effects. While the idea behind N-of-1 trials might seem simple, analyzing and interpreting N-of-1 trials is not straightforward. Here we ground N-of-1 trials in a formal causal inference framework and formalize intuitive claims from the N-of-1 trials literature. We focus on…

用变换与增广最小二乘估计地球温度响应AI 解读

The long-term relationship between radiative forcing and surface temperature is central to predicting the impacts of climate change. This study employs multicointegration to characterize this relationship and provides the first application of the Transformed and Augmented Ordinary Least Squares (TAOLS) estimator to estimate the multicointegration model. The main objective is to estimate the Equilibrium Climate Sensitivity (ECS), defined as the global mean surface temperature…

基于核岭回归的条件平均处理效应迁移学习AI 解读

The proliferation of data has sparked significant interest in leveraging findings from one study to estimate treatment effects in a different target population without direct outcome observations. However, the transfer learning process is frequently hindered by substantial covariate shift and limited overlap between (i) the source and target populations, as well as (ii) the treatment and control groups within the source. We propose a novel method for overlap-adaptive…

小样本与短面板：用贴近实际的数据评估政策评价方法AI 解读

Methods for estimating causal effects in longitudinal, quasi-experimental settings are widely used in economics, public health, political science, and other fields. However, studies evaluating effects of health policies often rely on limited sample sizes, both in terms of study units and time periods analyzed. Synthetic difference-in-differences (SDiD) and the augmented synthetic control method (ASCM) are recently developed methods for evaluating the effects of policy…

利用随机分配与观察网络数据估计社会效应AI 解读

This paper introduces an innovative approach to identifying and estimating the parameters of interest in the widely recognized linear-in-means regression model under conditions where the initial randomization of peers determines the observed network. We assert that peers who are initially randomized do not produce social effects. However, after randomization, agents can endogenously develop significant connections that potentially generate peer influences. We present a…

离散双重难解问题的共轭广义贝叶斯推断AI 解读

Doubly intractable problems occur when both the likelihood and the posterior are available only in unnormalized form, with computationally intractable normalization constants. Bayesian inference then typically requires direct approximation of the posterior through specialized and typically expensive MCMC methods. In this paper, we provide a computationally efficient alternative in the form of a novel generalized Bayesian posterior that allows for conjugate, closed-form or…

可再生高维期望损失回归AI 解读

Expected Shortfall (ES) has become a core coherent risk measure in finance and statistics, and high-dimensional ES regression is crucial for characterizing heterogeneous tail risk with massive covariates. Existing offline methods for high-dimensional ES regression rely on access to full data, which fails under streaming data scenarios with sequential batch arrival and limited storage. To address this issue, this paper proposes a renewable estimation and inference framework…

基于采样的自适应主动学习：稳健的代理贝叶斯推断AI 解读

Bayesian inference is difficult when likelihood evaluations are expensive and budgets are limited. We propose sampling-based adaptive active learning (SALE), a Gaussian-process (GP) framework for surrogate-based Bayesian inference. SALE uses the expected posterior (EP) induced by normalised GP sample paths as a common sequential-design measure: it defines a posterior-guided search region and weights uncertainty reduction (UR). A state-dependent rule allocates evaluations…

事件时间结果因果亚组估计的贝叶斯层级自助框架AI 解读

Causal estimation of treatment effects within prespecified subgroups, such as biomarker-defined strata, disease phenotypes, or demographic groups are often of clinical interest. Bayesian approaches are attractive for subgroup effect estimation because flexible priors can represent complex treatment heterogeneity and propagate posterior uncertainty. Frequentist methods for prespecified causal subgroup analysis with time-to-event outcomes are also available, including…

右删失结果回归与推断的双重稳健学习器AI 解读

This paper introduces a general framework for doubly robust nonparametric regression with right-censored outcomes, adapting the strategy of the 'DR-learner' for heterogeneous treatment effects to the censored data setting. Our method generalizes censoring unbiased transformations to generate pseudo-outcomes, which serve as inputs for a second-stage nonparametric regression with sample-splitting or cross-fitting. We derive a novel representation of the conditional bias of…

整数值自回归模型的有符号矩阵稀疏化与投影估计AI 解读

Integer-valued time series are ubiquitous in fields such as finance, economics, and epidemiology. As spatiotemporal data structures in these domains grow increasingly complex and high-dimensional, the matrix integer-valued autoregressive (MINAR) model efficiently captures row-column cross-correlations to reduce dimensionality. However, it fundamentally fails to accommodate negative values, which is a critical flaw for analyzing real-world differenced data or financial tick…

复杂 SVAR 的依赖野生自助推断：波动率工具变量应用AI 解读

We propose a dependent wild bootstrap method based on local projections for computing the joint asymptotic distribution of parameter estimates in structural vector autoregression models. This procedure can be applied to the raw data in levels without pretesting while remaining robust to unit roots, cointegration, polynomial trends, and conditionally heteroscedastic shocks in a general form. We show how knowledge of the joint asymptotic distribution in persistent data setups…

稳健工具变量：对抗污染下的精确收敛率与推断AI 解读

Because 2SLS is built from sample averages, a small number of observations can have a disproportionate effect on estimates and inference. We introduce W-2SLS, a simple drop-in robustification that replaces these averages by quantile-winsorized means. We analyze W-2SLS under adversarial contamination, which permits both the identities and the reported values of the contaminated observations to depend on the realized clean sample and therefore accommodates targeted or…

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…

跨单位溢出的非参数检验AI 解读

Cross-unit dependence is pervasive in empirical applications and complicates econometric inference, especially when spillovers operate in nonlinear ways. We propose a novel nonparametric test for cross-unit spillovers that may operate through peers' attributes, peers' outcomes, or both. The test is straightforward to implement, as it requires only estimation under the null hypothesis of no cross-unit spillovers, and is shown to have a convenient asymptotic standard normal…

随机对象独立性与条件独立性检验的距离轮廓嵌入AI 解读

Testing independence or conditional independence is fundamental to statistical inference, yet existing methods for non-Euclidean random objects often face a difficult trade-off between geometric flexibility and theoretical tractability. We introduce the Distance Profile Embedding (DPE), a novel representation that maps random objects from general metric spaces into a Hilbert space of square-integrable functions. We prove that this mapping is injective and preserves full…

固定阶数 PCA：因子数量高估时的理论AI 解读

We develop asymptotic theory for principal component analysis (PCA) of a high-dimensional factor model in which the working dimension $R$ is fixed and only required to satisfy $R \ge r$, where $r$ is the true number of factors. Building on anisotropic local laws from random matrix theory, we show that the ``extra'' empirical eigencomponents beyond the $r$-th are asymptotically noise-governed, incoherent, and nearly orthogonal to the factor loadings. We introduce two…

核岭回归预测误差的置信上界：基于高斯重拟合AI 解读

Assessing a single model fit requires a computable upper confidence bound for the gap between the fit and the unknown truth, as mean estimates ignore realization variance. Standard cross-validation margins are bottlenecked at order $n^{-1/2}$ by noise fluctuations, even when the true error shrinks faster. While wild refitting cancels this noise level, existing Rademacher sign methods degenerate for kernel ridge regression and rely on unobservable quantities. We propose a…

个体化治疗规则的贝叶斯中介分析AI 解读

The value of an individualized treatment rule (ITR), defined as the expected outcome under treatment assignment according to the rule, is useful for assessing average clinical benefit but does not explain how the benefit of a rule is generated. We propose a causal mediation framework for decomposing the value contrast between a prespecified candidate ITR and a clinically meaningful reference rule into direct and indirect components. Using rule-specific nested potential…

多重呼吸道检测时代如何选择检测阴性对照：因果视角AI 解读

The test-negative design (TND) is widely used to estimate vaccine effectiveness (VE) for respiratory pathogens by comparing vaccination odds among test-positive cases versus test-negative controls. A central yet underexplored design element is which test-negative illnesses constitute valid controls. With rapid multiplex PCR panels, investigators can now identify specific non-focal pathogens among test-negative patients, allowing for better characterization of ``test-negative…

时间序列中的动态局部平均处理效应AI 解读

This paper discusses identification, estimation, and inference on dynamic local average treatment effects (LATEs) in instrumental variables (IVs) settings. First, we show that compliers--observations whose treatment status is affected by the instrument--can be identified individually in time series data using smoothness assumptions and local comparisons of treatment assignments. Second, we show that this result enables not only better interpretability of IV estimates but…

干扰下不依赖暴露映射的通用因果推断框架AI 解读

We develop a general framework for design-based causal inference under interference in cluster experiments conducted via two-stage randomization on a network of interconnected units, without relying on exposure mapping assumptions, exclusion of cross-cluster interference, or Bernoulli treatment assignments. Within this framework, we establish a complete characterization of linear weighted estimators (LW) as defined by Godambe (1955) that achieve identification of various…