论文检索

稀疏随机张量的锐谱范数集中与正则化AI 解读

We prove sharp spectral-norm bounds and construct a mean-fitting regularization procedure for sparse random tensors of fixed order $k$. For an $n\times\cdots\times n$ tensor with independentBernoulli$(p)$ entries, the centered norm is $O(\sqrt{np})$ with polynomially high probability whenever $np\ge c\log n$. This removes the logarithmic loss in Zhou and Zhu (2021). We also determine the expected centered norm at every sparsity level: for every $0<p\le1/2$, it has order $\min\{n^kp,\sqrt{d_{\mathrm{eff}}}\}$…

因果可解释性的估计与推断AI 解读

Understanding how much each variable contributes to an outcome is a central question across disciplines. A causal view of explainability is favorable for its ability in uncovering underlying mechanisms and generalizing to new contexts. Based on a family of causal explainability quantities, we develop methods for their estimation and inference. In particular, we construct a one-step correction estimator using semi-parametric efficiency theory, which explicitly leverages the…

生存与广义线性结局下可解释亚组的识别AI 解读

A subgroup analysis searching a trial's covariates or many prespecified subgroups often singles out a subgroup for its extremity, interpreted as if prespecified. Selection inflates the estimate; for a given selection rule, a correction removes the inflation. Two questions belong to the rule: which candidate to return, and whether to declare none. A third is whether inference extends to the patients left outside. We treat all three for interpretable subgroups, defined by…

Uniform confidence bands are widely used to quantify uncertainty about unknown functions in nonparametric inference. Although various methods, including the multiplier bootstrap, have been developed and theoretically justified, practical implementations typically replace the supremum over a continuum by the maximum over a finite evaluation grid. This paper investigates how this discretization affects coverage accuracy and how fine the evaluation grid must be to ensure valid…

Causal discovery is the subfield of causal inference concerned with estimating the structure of cause-and-effect relationships in a system of interrelated variables, as opposed to quantifying the strength or describing the form of causal effects. As interest in causal discovery builds in fields such as ecology, public health, and environmental sciences, where data are regularly collected with spatial and temporal structures, approaches must evolve to manage autocorrelation…

个体因果效应的局部保形预测AI 解读

Standard CATE estimators become inadequate under strong treatment-effect heterogeneity: confidence intervals for conditional means need not cover individual counterfactual effects. We propose an Individualized Causal Prediction (ICP) framework that constructs finite-sample valid conformal prediction intervals for the individual causal effect of a specific query unit. The method localizes calibration to a causally relevant neighborhood using cosine similarity weighted by…

We develop theory for the identification of complex causal queries from non-IID data. We propose a formal description of stochastic systems based on symmetries in data leaving causal mechanisms invariant. These symmetries become the source of statistical repetition for identification and mandate knowledge-transfer from observations to queries. The result is an abstract, simple and very general language for causal reasoning. We give descriptions of models and queries, and the…

Tobit 模型设定检验的乘子自助得分检验AI 解读

In this paper, we propose a simple score-based specification test for the Tobit regression model, implemented through a computationally efficient multiplier bootstrap. We test correct specification through the conditional moment restriction that the generalized Tobit residual has zero conditional mean, and construct a feasible score-based empirical process using the integrated conditional moments approach, conditioning on the estimated index so that no smoothing or tuning…

基于测度指标核岭回归的非参数上下文学习与推断AI 解读

In-Context learning enables an estimator, trained across multiple tasks, to adapt to a new unseen task solely through its in-context samples, without any parameter refitting. Despite growing interest in the statistical foundations of in-context learning, two gaps still remain. First, existing convergence results do not characterize the distributional behavior of the in-context learning estimators. Second, classical nonparametric methods are not designed to handle the…

This paper establishes a unified non-linear regularization framework for high-dimensional $M$-estimation, encompassing both linear regression and Cox's proportional hazards models. Rather than relying on traditional additive non-convex penalties, the proposed paradigm embeds sparsity directly into the parameter mapping using a smooth ($C^2$) component-wise ReParametrization map for Sparsity (RePS). This geometric formulation dynamically adapts to local parameter scales,…

含潜变量的高斯线性结构方程模型中数据驱动的有向无环图学习AI 解读

Structural equation modeling is typically performed in a confirmatory manner, requiring a priori specification of both the measurement model and the path diagram connecting the structural variables. In this paper, we study a semi-exploratory setting within a general Gaussian linear structural equation model with latent variables, where the loading structure in the measurement model is known, but the structural path diagram follows an unknown directed acyclic graph (DAG) to…

最优传输泛函的中心极限定理AI 解读

We establish central limit theorems for empirical plug-in estimators of scalar functionals of quadratic optimal transport maps and potentials, including Wasserstein costs and several risk measures constructed from transport-based multivariate quantiles. We study the one-sample problem with a known reference distribution and i.i.d. observations from the target distribution. A natural approach is to combine a limit theorem for the empirical transport map with the functional…

基于Gamma散度的稳健稀疏个性化联邦学习AI 解读

To address the challenges of statistical heterogeneity across clients and within-client data contamination in federated learning, we propose a robust and sparse personalized federated learning method based on the gamma-divergence, termed Gamma-PFL. Specifically, we combine a loss function based on the gamma-divergence with fusion and sparse regularization terms to exploit similarities in regression coefficients across clients while enabling robust estimation of personalized…

网络干扰下依赖分配的辅助变量：重随机化与高维调整AI 解读

Network experiments are widely used to evaluate interventions in settings such as vaccine trials and social networks, where interference is often present. In these settings, predictive auxiliary variables, such as treatment-weighted neighbor summaries, depend on treatment assignments and generally exhibit nonzero exposure effects. In this paper, we propose rerandomization and covariate adjustment methods based on estimation errors in these auxiliary exposure effects to…

How can we conduct causal inference when the outcome of interest is latent and imperfectly measured by multiple indicators? We develop a nonparametric framework for identifying and estimating average treatment effects on latent outcomes in randomized experiments. Researchers commonly aggregate multiple outcome indicators into a summary measure before estimating treatment effects. We identify a fundamental but often overlooked comparability problem arising from this practice:…

高维块状非光滑正则化下近似留一法的精度AI 解读

Approximate leave-one-out (ALO) refers to methods that provide computationally efficient and statistically accurate approximations to leave-one-out risk estimation. These methods have recently attracted increased attention because of their connections to other important problems, including machine unlearning and data valuation. For empirical risk minimization, ALO methods often rely on a few Newton steps and therefore require smooth loss functions and regularizers. In…

整群随机试验中不依从下直接与溢出效应的贝叶斯机器学习方法AI 解读

We develop a Bayesian machine learning approach for estimating direct and spillover effects in cluster-randomized trials with treatment noncompliance. To flexibly model outcomes and treatment receipt while accounting for within-cluster dependence, we combine Bayesian additive regression trees with cluster-level random effects. We augment the outcome regression with a peer-treatment clever covariate, defined as the predicted mean proportion of treated peers from a preliminary…

宏观经济学中的循证政策选择AI 解读

This paper studies data-driven macroeconomic policy choice in a structural VAR framework. We formulate the policymaker's problem of optimizing the future path of a policy instrument as a statistical decision problem under two alternative policy regimes: a "contemporaneous" regime, where the chosen path is implemented through unexpected policy shocks each period, and a "forward guidance" regime, where the expected path is announced in advance and enforced through anticipated…

基于LSTM模型的非迭代条件期望g公式插件估计量AI 解读

The non-iterative conditional expectation (NICE) plug-in estimator of the g-formula can be used to estimate the causal effects of sustained treatment strategies, even in the presence of treatment-confounder feedback, under the assumptions of exchangeability, positivity, and consistency. In practical applications, parametric models are commonly used to estimate the joint distribution of confounders, treatments, and outcomes over time. As an alternative to parametric models,…

This paper studies social interactions in discrete choice models in which individuals can respond differently to different network members, conforming to some while distinguishing from others. In this generalized framework, I show how to use within-network variation in individual choices to draw inference about differences in endogenous interaction effects, while accounting for unobserved contextual effects. I also show how to construct internal instruments to correct for…

复杂化是万灵药吗？随机特征模型在金融预测中的学习能力AI 解读

This paper investigates the ability of random feature models to learn more complex patterns than the simple negative autocorrelation of financial returns. Theoretical insights highlight that the learning capacity of random features ridge regression is determined by the interplay between signal-to-noise ratio (SNR) level and regularization. We characterize the different learning regimes through the derivation of a particular threshold: the SNR frontier. Practically, our…

仅分位数披露下的回归：推断与部分识别AI 解读

Researchers often observe group quantiles but not group means, raising the question of whether these summaries alone support conventional regression analysis. We develop estimation and inference methods for linear models of group means under quantile-only and mixed disclosure. We establish conditions on disclosure design for consistent estimation and asymptotic normality at the parametric rate. Under fixed disclosure, we characterize sharp identified sets and construct…

高维协变量辅助的排序数据建模AI 解读

Ranking data in modern applications often involves covariate vectors whose dimension grows with the number of objects. Identifying relevant covariates and integrating them with item-specific utilities can yield better predictive performance. In this work, we study a Plackett--Luce model that incorporates both item-specific utilities and high-dimensional covariate effects. Under a sparsity assumption on the covariate coefficients, we establish a necessary and sufficient…

纵向结局与替代标志物联合评价的因果状态空间框架AI 解读

Surrogate markers offer the potential to reduce the burden of data collection by replacing costly or invasive primary outcomes with more accessible measurements, provided that they can faithfully indicate the effectiveness of a treatment. However, appropriate evaluation of a surrogate is particularly complex in longitudinal studies, where both outcomes and surrogates can evolve dynamically over time and interest lies not only in the treatment effect at one time, but rather…