每日简报

约束近等调图融合 Lasso 的渐近分布AI 解读

This paper studies the asymptotic distribution of a constrained lasso-type estimator for denoising signals defined on the nodes of a graph, where the underlying structure encodes relationships between variables. We show that, under suitable assumptions on the penalization parameters, the limiting distribution of the estimator is obtained by applying the corresponding constrained procedure to the asymptotic distribution of the unrestricted estimator. Thus, the constrained…

重访确定性计算机实验的惩罚似然估计AI 解读

Gaussian processes (GPs) are popular as nonlinear regression models for expensive computer simulations, yet GP performance relies heavily on estimation of unknown covariance parameters. Maximum likelihood estimation (MLE) is common, but it can be plagued by numerical issues in small data settings. The addition of a nugget may help but is not a cure-all. Penalized likelihood methods may improve upon traditional MLE, but their success depends on tuning parameter selection. We…

老年人为何储蓄？利用健康冲击揭示遗赠动机AI 解读

We revisit the saving behavior of elderly singles using an adversarial structural estimation framework by Kaji, Manresa and Pouliot (2023). The method bridges the simulated method of moments (SMM) and maximum-likelihood estimation by embedding a flexible discriminator, implemented as a neural network, that adaptively selects the most informative features of the data. Applying this approach to the model of De Nardi, French, and Jones (2010) with AHEAD data, we show that…

利用机器学习分解不平等并处理共同支撑问题AI 解读

The Kitagawa-Oaxaca-Blinder decomposition splits the difference in means between two groups into an explained part, due to observable factors, and an unexplained part. In this paper, we reformulate this framework using potential outcomes, highlighting the critical role of the reference outcome. To address limitations like common support and model misspecification, we extend Neumark's (1988) weighted reference approach with a doubly robust estimator. Using Neyman…

度量统计视角下的随机集AI 解读

Since the seminal work by Beresteanu and Molinari(2008), the random set theory and related inference methods have been widely applied in partially identified econometric models. Meanwhile, there is an emerging field in statistics for studying random objects in metric spaces, called metric statistics. This paper clarifies a relationship between two fundamental concepts in these literatures, the Aumann and Fréchet means, and presents some applications of metric statistics to…

共形时间序列预测入门AI 解读

Conformal prediction is a powerful post-hoc framework for uncertainty quantification that provides distribution-free coverage guarantees. However, these guarantees crucially rely on the assumption of exchangeability. This assumption is fundamentally violated in time series data, where temporal dependence and distributional shifts are pervasive. As a result, classical split-conformal methods may yield prediction intervals that fail to maintain nominal validity. This review…

随机森林与集成方法的经验似然推断AI 解读

We develop an empirical likelihood (EL) framework for random forests and related ensemble methods, providing a likelihood-based approach to quantify their statistical uncertainty. Exploiting the incomplete $U$-statistic structure inherent in ensemble predictions, we construct an EL statistic that is asymptotically chi-squared when subsampling induced by incompleteness is not overly sparse. Under sparser subsampling regimes, the EL statistic tends to over-cover due to loss of…

合成生存控制：面向事件发生时间反事实的合成控制扩展AI 解读

Estimating causal effects on time-to-event outcomes from observational data is particularly challenging due to censoring, limited sample sizes, and non-random treatment assignment. The need for answering such "when-if" questions--how the timing of an event would change under a specified intervention--commonly arises in real-world settings with heterogeneous treatment adoption and confounding. To address these challenges, we propose Synthetic Survival Control (SSC) to…

局部平稳时间序列的迁移学习与局部线性回归AI 解读

This paper investigates locally linear regression for locally stationary time series and develops theoretical results for locally linear smoothing and transfer learning. Existing analyses have focused on local constant estimators and given samples, leaving the principles of transferring knowledge from auxiliary sources across heterogeneous time-varying domains insufficiently established. We derive uniform convergence for multivariate locally linear estimators under strong…

连续处理因果推断中的个体化预测带AI 解读

Individualized treatments are crucial for optimal decision making and treatment allocation, specifically in personalized medicine based on the estimation of an individual's dose-response curve across a continuum of treatment levels, e.g., drug dosage. Current works focus on conditional mean and median estimates, which are useful but do not provide the full picture. We propose viewing causal inference with a continuous treatment as a covariate shift. This allows us to…

利用疫苗无关感染去偏随时间变化的风险率型疫苗效应AI 解读

Understanding how vaccine effectiveness (VE) changes over time can provide evidence-based guidance for public health decision making. While commonly reported by practitioners, time-varying VE estimates obtained using Cox regression are vul- nerable to hidden biases. To address these limitations, we describe how to leverage vaccine-irrelevant infections to identify hazard-based, time-varying VE in the pres- ence of unmeasured confounding and selection bias. We articulate…

金融事件研究中的因果推断AI 解读

Financial event studies, ubiquitous in finance research, typically use linear factor models with known factors to estimate abnormal returns and identify causal effects of information events. This paper demonstrates that when factor models are misspecified -- an almost certain reality -- traditional event study estimators produce inconsistent estimates of treatment effects. The bias is particularly severe during volatile periods, over long horizons, and when event timing…

稳健贝叶斯因果推断AI 解读

This paper develops a Bayesian framework for robust causal inference from longitudinal observational data. Many contemporary methods rely on structural assumptions, such as factor models, to adjust for unobserved confounding, but they can lead to biased causal estimands when mis-specified. We focus on directly estimating time--unit--specific causal effects and use generalised Bayesian inference to quantify model mis-specification and adjust for it, while retaining…

通过张量补全推断序贯处理的因果效应AI 解读

Marginal Structural Models (MSMs) are popular for causal inference of sequential treatments in longitudinal observational studies, which however are sensitive to model misspecification. To achieve flexible modeling, we envision the potential outcomes to form a three-dimensional tensor indexed by subject, time, and treatment regime and propose a tensorized history-restricted MSM (HRMSM). The semi-parametric tensor factor model allows us to leverage the underlying low-rank…

交叉平衡：利用数据设计并高效分析观察研究AI 解读

Causal inference starts with a simple idea: compare groups that differ by treatment, not much else. Traditionally, similar groups are constructed using only observed covariates; however, it remains a long-standing challenge to incorporate available outcome data into the study design while preserving valid inference. In this paper, we study the general problem of covariate adjustment, effect estimation, and statistical inference when balancing features are constructed or…

贝叶斯半参数因果推断：处理效应的目标化双重稳健估计AI 解读

We propose a semiparametric Bayesian methodology for estimating the average treatment effect (ATE) within the potential outcomes framework using observational data with high-dimensional nuisance parameters. Our method introduces a Bayesian debiasing procedure that corrects for bias arising from nuisance estimation and employs a targeted modeling strategy based on summary statistics rather than the full data. These summary statistics are identified in a debiased manner,…

将右删失事件视作处理的讨论AI 解读

In causal inference literature, potential outcomes are often indexed by the "elimination of all right-censoring events," leading to the perception that such a restriction is necessary for defining well-posed causal estimands. In this paper, we clarify that this restriction is not required: a well-defined estimand can be formulated without indexing on the elimination of such events. Achieving this requires a more precise classification of right-censoring events than has…

迭代边际化贝叶斯映射进行似然最大化及其非线性面板模型应用AI 解读

Complex dynamic systems can be investigated by fitting mechanistic stochastic dynamic models to time series data. In this context, commonly used Monte Carlo inference procedures for model selection and parameter estimation quickly become computationally unfeasible as the system dimension grows. The increasing prevalence of panel data, characterized by multiple related time series, therefore necessitates the development of inference algorithms that are effective for this…

单变点检验方法AI 解读

Single changepoint tests have become a staple check for homogeneity of a climate time series, suggesting how climate has changed should non-homogeneity be declared. This paper summarizes the most prominent single changepoint tests used in today's climate literature, relating them to one and other and unifying their presentations. Asymptotic quantiles for the individual tests are presented. Derivations of the quantiles are given, enabling the reader to tackle cases not…

为何因果推断的双重稳健估计量在方差估计上并非双重稳健AI 解读

Doubly robust estimators (DRE) are widely used in causal inference because they yield consistent estimators of average causal effect when at least one of the nuisance models, the propensity for treatment (exposure) or the outcome regression, is correct. However, double robustness does not extend to variance estimation; the influence-function (IF)-based variance estimator is consistent only when both nuisance parameters are correct. This raises concerns about applying DRE in…

高维非线性向量自回归模型的估计AI 解读

High-dimensional vector autoregressive (VAR) models have numerous applications in fields such as econometrics, biology, climatology, among others. While prior research has mainly focused on linear VAR models, these approaches can be restrictive in practice. To address this, we introduce a high-dimensional non-parametric sparse additive model, providing a more flexible framework. Our method employs basis expansions to construct high-dimensional nonlinear VAR models. We derive…

单连接点变点模型的渐近分布AI 解读

A single joinpoint changepoint model partitions a time series into two segments, joined at the changepoint time by constraining the estimated piecewise linear regression responses to be continuous. This manuscript derives the exact asymptotic distribution of the changepoint existence test statistic gauging whether or not a second segment is necessary. The identified asymptotic distribution, a supremum of a Gaussian process over the unit interval, is rather unwieldy. The work…

隐马尔可夫模型中的灵活单峰密度估计AI 解读

1. Hidden Markov models (HMMs) are powerful tools for modelling time-series data with underlying state structure. However, selecting appropriate parametric forms for the state-dependent distributions is often challenging and can lead to model misspecification. To address this, P-spline-based nonparametric estimation of state-dependent densities has been proposed. While offering great flexibility, these approaches can result in overly complex densities (e.g. bimodal) that…

本地收益大、净收益小：波多黎各首次行业进入的直接与溢出效应AI 解读

I study how first sizable industry entries reshape local and neighboring labor markets in Puerto Rico. Using over a decade of quarterly municipality--industry data (2014Q1--2025Q1), I identify ``first sizable entries'' as large, persistent jumps in establishments, covered employment, and wage bill, and treat these as shocks to local industry presence at the municipio--industry level. Methodologically, I combine staggered-adoption difference-in-differences estimators that are…