论文检索

非参数生成离散类型下的有效 GMM 推断AI 解读

The recent progress in machine learning and classification methods has enabled researchers to predict economic discrete attributes that are not observed in standard datasets. These range from missing demographics, such as ethnicity and poverty status, to economic categories, such as innovative firms, metropolitan areas, occupational groups, binary treatment status, and counterfactual status. Although the plug-in procedure is widely used in empirical applications,…

零交叉与精度—平滑前沿：极大单调与极小拐点非平稳预测器AI 解读

The Smooth Sign Accuracy (SSA) framework resolves the smoothness--accuracy dilemma for stationary predictors by balancing mean squared error (MSE) against the frequency of sign changes. We generalize it to non-stationary integrated processes (I-SSA), deriving MSE-optimal predictors constrained by the expected duration between turning points (I(1)) or inflection points (I(2)). The resulting maximal-monotone and minimal-inflection predictors, filters, and smoothers customize…

强化学习的统计基础：非渐近视角AI 解读

Reinforcement learning (RL) embodies a foundational paradigm for sequential decision-making in the face of uncertainty. The rapid growth of model complexity in emerging applications has significantly amplified the challenges of achieving data-efficient RL, particularly in sample-starved scenarios where data collection may be costly, time-consuming, or even high-stakes. Consequently, understanding and improving the data efficiency of RL algorithms has become a central problem…

统一数据自适应纵向治疗效应亚组发现与治疗前成员预测AI 解读

Precision medicine seeks to match patients with treatments from which they are most likely to benefit. A key step toward this goal is to learn treatment-effect heterogeneity from longitudinal study data. This requires discovering subgroups with distinct treatment-effect trajectories and predicting subgroup membership for new patients from pretreatment information. Existing methods do not fully unify these tasks. Covariate-informed membership models typically prespecify the…

高维分析数据中潜变量回归的观测特定不确定性建模AI 解读

Across many fields, including medical diagnostics, process monitoring, and quality control, decisions often rely on point predictions for single samples. Observation-specific uncertainty is needed to assess the risk of decision error when predictions are compared with thresholds. Latent-variable regression is widely used for such predictions, but observation-specific uncertainty remains difficult to estimate. Traditional methods assume unbiased predictions, attributing…

协变量调整之外，再随机化是否仍有帮助？理论与实践指南AI 解读

Rerandomization is a modern experimental design technique that repeatedly randomizes treatment assignments until covariates are deemed balanced between treatment groups. This enhances the precision and coherence of causal effect estimators, mitigates false discoveries from p-hacking, and increases statistical power. Recent work suggests that balancing covariates via rerandomization does not alter the asymptotic precision of covariate-adjusted estimators, thereby making it…

重叠有限下的功能特定信息借用AI 解读

Source observations can improve a target average without estimating the entire shared response accurately. We study this distinction in two independent observational samples whose treatment effects share a rough component and differ by a smoother discrepancy. For power-law treatment overlap known for the analysis, known design density, fixed positive Sobolev radii, and Gaussian noise, we derive matching minimax mean squared error bounds with three regimes. Two explicit…

回归残差中的协方差识别AI 解读

Regression residuals are projections of the errors, not observations of them. This paper characterizes what residuals identify about the error covariance. Residualization acts on the covariance through a known linear map, and under an unrestricted covariance model the directions it destroys are exactly those generated by the column space of the regressors. A linear covariance functional is identified if and only if it does not load on these lost directions, and restricting…

利用不完整辅助信息的小域估计AI 解读

Auxiliary information is increasingly available from administrative and other data sources, but it is often incomplete and of non-probability origin. We propose a two-step small area estimation approach in which the first step relies on design-based model calibration and exploits a large non-probability sample providing a noisy proxy of the study variable for only part of the population. A unit-level measurement-error working model is fitted on the linked overlap between the…

拓扑处理效应的不确定性量化AI 解读

We present a unified framework for uncertainty quantification over filtration scale, extending the topological average treatment effect (TATE) analysis of \citet{kim-lee} to conditional mean confidence and individual effect prediction. For the conditional topological average treatment effect (CTATE), we derive multiplier confidence bands at a fixed covariate value, simultaneous over filtration scale, and prove validity in a correctly specified finite-dimensional regression…

稠密测量误差下的网络溢出效应估计AI 解读

This paper analyzes spillover effects in spatial (network) models when the neighborhood (adjacency) matrix is contaminated by measurement error from reporting, aggregation, or disclosure imperfections, leading to inconsistent estimation of network effects. We introduce a regularization framework for the latent network that allows for sparse and/or low-rank structure and accommodates potential correlation between measurement errors and outcomes. We propose two estimators: (i)…

Probit 回归中的强对数凹性AI 解读

We show that strong log-concavity emerges in probit regression likelihoods without ridge penalization (i.e. Gaussian priors), unlike for the logistic case. Specifically, we provide: (a) a non-asymptotic characterization of strong log-concavity for fixed designs, similar to that for the existence of the maximum likelihood estimator (MLE) and (b) an asymptotic analysis for random designs, in the proportional regime when the sample size $n$ and the number of covariates $d$ grow…

xtlp：面板局部投影的新 Stata 命令AI 解读

Panel local projection (LP) estimated with fixed effects (FEs) is widely used to evaluate the dynamic effects of economic shocks across countries or regions. However, FE estimation of panel LP models is subject to Nickell bias arising from the predictive structure of LP specifications, even when lagged dependent variables are not included in the regressors. This bias invalidates standard inference based on conventional t-statistics. We introduce xtlp, a new Stata command…

异方差识别的一条引理：部分识别、特征值构造与秩检验警示AI 解读

This teaching note unifies known results on identification through heteroskedasticity, under volatility regimes, GARCH, stochastic volatility and covariate-driven variances, in one lemma with a short linear-algebra proof. A shock is identified up to sign and labelling if and only if no other shock shares its variance path, and shocks that share a path are identified up to a rotation. The rows of the structural coefficient matrix solve one generalised eigenvalue problem that…

双对角估计量：降低双边市场实验中的偏差AI 解读

Interference between participants in two-sided marketplace experiments can severely bias conventional A/B tests. While two-sided randomization (TSR), which randomizes both supply and demand, enables estimators that mitigate this bias, existing analyses offer limited guidance on which estimator to prefer for the balanced market conditions common in practice, as they largely focus on extreme supply- or demand-constrained regimes. We introduce the Double Diagonal (DD)…

解算子的工具变量迁移学习AI 解读

Data-driven learning for the solution operator of a differential equation has broad scientific importance, but observations from a target system are often scarce and costly to collect. Related source systems may provide abundant observations under similar physical conditions, whereas even small changes in the governing equation can induce complex changes in the solution operator, making naive data pooling ineffective. We propose a structured transfer learning method that…

利用连续性对连续处理进行协变量平衡AI 解读

We consider causal effect estimation in observational studies with continuous treatment. We introduce a general class of causal estimands that, as a special case, includes the average causal effect of assigning treatment according to an arbitrary counterfactual distribution. Balancing estimators are particularly attractive tools for covariate adjustment in this setting because they avoid directly modeling the treatment assignment mechanism, thereby reducing the potential for…

卷积偏 t 分布AI 解读

We introduce the convolution skew-$t$ (CST) distribution, which combines independent standardized skew-$t$ subvectors through a general loading matrix to accommodate heterogeneous tails, directional asymmetry, and non-elliptical dependence. We establish identification and develop likelihood inference using analytical scores and a one-dimensional reduction of Fisher information. For the finite-variance model, we prove consistency and asymptotic normality of maximum likelihood…

超越预趋势：基于反事实填补不一致性的双重差分敏感性分析AI 解读

In the canonical Difference-in-Differences design, the control group's post-treatment change serves as an imputation of the treated group's counterfactual change in the same period, an imputation justified by parallel trends. However, differences in group composition can produce between-group differences in how outcomes would evolve over time, rendering this imputation vulnerable to confounding. An alternative imputation -- such as one based on the treated group's…

中介变量受检测定量下限左删失时的因果间接效应评估AI 解读

Causal mediation analysis is essential for disentangling the mechanisms by which investigational therapeutic and preventive agents impact clinical outcomes. However, the measurement of biological mediators is often subject to left-censoring by technical measurement limitations, most commonly an assay's limit of quantification. This form of censoring can pose severe challenges for both identification and estimation of causal mediation estimands, particularly when the…

面向多峰目标测度的通用后采样重加权方法AI 解读

When sampling multi-modal probability distributions, correctly estimating the relative probability of each mode, even when the modes have been discovered and locally sampled, remains challenging. We test a simple reweighting scheme designed for this situation, which consists in minimizing (in terms of weights) the Kullback-Leibler divergence of a weighted (regularized) empirical distribution of the samples with respect to the target measure. We show in a theoretical toy…

含噪声充分统计量下的最优政策AI 解读

We study how policy-makers should set $k$ policy instruments so as to best hit $m$ (perhaps dynamic) targets, in a linear-quadratic model where there is statistical uncertainty about the causal effects of the policies. Plugging the estimated effects into "sufficient statistics" optimal policy formulas, as commonly done in the literature, can result in infinite risk. In fact, maintaining the status quo regardless of the data is uniquely minimax-optimal when $m>1$, and…

全维高斯混合模型 NPMLE 的自正则化与结构自适应AI 解读

We establish structural properties of the nonparametric maximum likelihood estimator (NPMLE) for $d$-dimensional Gaussian mixtures whose means lie in a hypercube. We show that, with high probability, the NPMLE based on $n$ independent observations self-regularizes by fitting a mixture model with $O((\frac{\log n}{\log\log n})^d)$ components, achieving the optimal model complexity without explicit model selection. Furthermore, the NPMLE exhibits a structural adaptation…

随机块模型中的率最优谱似然比聚类AI 解读

Spectral clustering commonly assigns communities by clustering a low-dimensional representation, whereas likelihood refinement can improve an initial assignment using the observed edges. We ask whether sharp community-recovery accuracy can be retained when the refinement no longer accesses individual edges. We develop spectral likelihood-ratio clustering for stochastic block models using one regularized adjacency eigensystem and the original vertex degrees. The method…