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局部差分隐私约束下的双样本检验AI 解读

SUMMARY We consider the problem of two-sample testing under a local differential privacy constraint using a permutation procedure. We develop testing procedures that are optimal up to logarithmic factors for general discrete distributions and continuous distributions subject to a smoothness constraint. Both noninteractive and interactive tests are considered, and we show that allowing interactivity results in an improvement in the minimax separation rates. Our results show…

协变量分布迁移下的自动去偏机器学习AI 解读

Summary We present machine learning estimators for causal and predictive parameters under covariate shift, where covariate distributions differ between training and target populations. One such parameter is the average effect of a policy that alters the covariate distribution, such as a treatment that modifies surrogate covariates used to predict long-term outcomes. Another example is the average treatment effect for a population with a shifted covariate distribution. We…

符号网络平衡性的非参数推断AI 解读

Abstract In many real-world networks, relationships often go beyond simple dyadic presence or absence; they can be positive, such as friendship, alliance and mutualism, or negative, characterized by enmity, disputes and competition. To understand the mechanisms of formation of such signed networks, social balance theory sheds light on the dynamics of positive and negative connections. In particular, it characterizes the proverbs ‘a friend of my friend is my friend’ and ‘an…

度量图上的非参数估计AI 解读

Abstract This work discusses a theory of functional spaces over metric graphs that permits the definition of penalized likelihood methods for data observed over spatial supports that are graphs. Within the considered mathematical framework, we recover classical results in functional analysis, such as a Poincaré-type inequality. This, in turn, enables us to uplift, to the present setting, the theory of some fundamental penalized likelihood methods. Specifically, we present…

条件于后验样本的灵活频率学派拟合优度检验AI 解读

Summary Tests of goodness of fit are used in nearly every domain where statistics is applied. One powerful and flexible approach is to sample artificial datasets that are exchangeable with the real data under the null hypothesis (but not under the alternative), as this allows the analyst to conduct a valid test using any test statistic they desire. Such sampling is typically done by conditioning on either an exact or an approximate sufficient statistic, but existing methods…

自适应实验的设计稳定性及其对处理效应估计的影响AI 解读

Abstract We study the problem of estimating the average treatment effect under sequentially adaptive treatment assignment mechanisms. In contrast to classical completely randomized designs, the setting we consider is one in which the probability of assigning treatment to each experimental unit may depend on prior assignments and observed outcomes. Within the potential outcomes framework (Neyman, 1923), we propose and analyse two natural estimators for the average treatment…

基于球散度的条件独立性度量与局部野自助检验AI 解读

Abstract In this paper we introduce a new measure of conditional dependence between two random vectors $ {\boldsymbol{X}} $ and $ {\boldsymbol{Y}} $, given another random vector $ \boldsymbol{Z} $ using the ball divergence. Our measure characterizes conditional independence and does not require any moment assumptions. We propose an estimator of the measure using a kernel-averaging technique and derive its asymptotic distribution. Using this estimator, we construct a test for…

随机试验中 Rosenbaum 秩估计量的稳健性与效率：设计视角AI 解读

Summary Mean-based estimators of causal effects in randomized experiments may behave poorly if the potential outcomes have a heavy tail or contain outliers. An alternative estimator proposed by Rosenbaum (1993) estimates a constant additive treatment effect by inverting a randomization test using ranks. We develop a design-based asymptotic theory for this rank-based estimator and study its robustness and efficiency properties. We show that Rosenbaum’s estimator is robust…

高维向量与张量时间序列的重尾稳健因子建模AI 解读

Summary We study the problem of factor modelling vector- and tensor-valued time series in the presence of heavy tails in the data, which produce extreme observations with nonnegligible probability. We propose combining a two-step procedure for tensor decomposition with data truncation, which is easy to implement and does not require an iterative search for a numerical solution. Departing from the light-tail assumptions often adopted in the time series factor-modelling…

高维主成分分析中显著成分数量的估计AI 解读

Abstract We consider the problem of estimating the number of significant components in high-dimensional principal component analysis. We propose a new penalized approach using the explained variance ratio and the rigidity of the nonspiked sample eigenvalues of sample covariance matrices of $ p $ variables. Compared with methods in the existing literature, the consistency of the proposed estimator holds, not only for independent data, but also for some times series data when…

模型误设下的稳健通用推断AI 解读

Summary In statistical inference, it is rarely realistic to assume that the hypothesized statistical model is well specified; consequently, it is important to understand the effects of misspecification on inferential procedures. When the hypothesized statistical model is misspecified, the natural target of inference is a projection of the data-generating distribution onto the model. We present a general method for constructing valid confidence sets for such projections,…

未观测混杂的平均强度敏感性分析AI 解读

Summary Sensitivity analysis for the unconfoundedness assumption is crucial in observational studies. For this purpose, the marginal sensitivity model has gained popularity in recent years owing to its good interpretability and mathematical properties. However, most existing models only consider a worst-case parameter that bounds the logit difference between the observed-data and full-data propensity scores, which may not fully capture the extent of unmeasured confounding.…

稀疏删失数据的函数型主成分分析AI 解读

Summary Functional principal component analysis is a key tool in the study of functional data, driving both exploratory analyses and feature construction for use in formal modelling and testing procedures. However, existing methods do not apply when functional observations are censored; for example, when the measurement instrument only supports recordings within a prespecified interval, thereby truncating values outside this range to the nearest boundary. A naïve application…

连续时变处理下边际结构模型的非参数有效估计AI 解读

Summary Marginal structural models are a popular method for estimating causal effects in the presence of time-varying exposures. In spite of their popularity, no scalable nonparametric estimator exists for marginal structural models with multi-valued or continuous time-varying treatments. In this paper, we combine flexible, data-adaptive regression methods, including ensemble learning techniques, with recent developments in semiparametric efficiency theory for longitudinal…

在控制错误发现率的同时识别分布差异AI 解读

Summary We consider the problem of comparing a reference distribution with several other distributions. Given a sample from both the reference and the comparison groups, we aim to identify the comparison groups whose distributions differ from that of the reference group. Viewing this as a multiple-testing problem, we introduce a methodology that provides exact, distribution-free control of the false discovery rate. To do so, we introduce the concept of batch conformal p…

叠加点过程的 Palm 分布及其统计推断AI 解读

Abstract Palm distributions play a central role in the study of point processes and their associated summary statistics. In this work, we characterize the Palm distributions of the superposition of independent point processes, establishing a simple mixture representation depending on the point processes’ Palm distributions and moment measures. We explore two statistical applications enabled by our main result. First, we consider minimum contrast estimation for corrupted…

序贯 Gibbs 后验及其主成分分析应用AI 解读

Summary Gibbs posteriors are proportional to a prior distribution multiplied by an exponentiated loss function, with a key tuning parameter that weights the information in the loss relative to the prior and provides control of posterior uncertainty. Gibbs posteriors provide a principled framework for likelihood-free Bayesian inference; however, in many situations, the inclusion of a single tuning parameter inevitably leads to poor uncertainty quantification. In particular,…

节点归属随时间变化的异配随机块模型动态聚类AI 解读

Summary We consider a time-ordered sequence of networks stemming from stochastic block models in which nodes gradually change their membership over time, and no network at any single time-point contains sufficient signal strength to recover its community structure. To estimate the time-varying community structure, we develop the kernel-debiased sum of squares method that performs spectral clustering after a debiased sum-of-squared aggregation of adjacency matrices. Our…

多处理与正值性违背下的因果参数比较AI 解读

Summary Comparing outcomes across treatments is essential in medicine and public policy. To do so, researchers typically estimate a set of parameters, possibly counterfactual, each targeting adifferent treatment. Treatment-specific means are commonly used, but their identification requires a positivity assumption: every subject has a nonzero probability of receiving each treatment. This assumption is often implausible, especially when treatment can take many values. Causal…

动态协变量平衡：利用潜在结果局部投影估计跨期处理效应AI 解读

Abstract This article concerns the estimation and inference of treatment effects in panel data settings when treatments change dynamically over time. We propose a balancing method that allows for (i) treatments to be assigned dynamically over time based on high-dimensional covariates, past outcomes and treatments; (ii) outcomes and time-varying covariates to depend on the trajectory of all past treatments; and (iii) heterogeneity of treatment effects. Our approach…

在超平面上刻画极端依赖AI 解读

Summary In this paper, we characterize the extremal dependence of $ d $ asymptotically dependent variables using a class of random vectors on the $ (d-1) $-dimensional hyperplane perpendicular to the diagonal vector $ \mathbf{1}=(1,\ldots,1) $. This translates analyses of multivariate extremes to analyses on a linear vector space, opening up possibilities for applying existing statistical techniques based on linear operations. As an example, we demonstrate how to obtain…

维度与样本量成比例时逻辑回归的 Diaconis–Ylvisaker 先验惩罚似然AI 解读

Summary We characterize the behaviour of the maximum Diaconis–Ylvisaker prior penalized likelihood estimator in high-dimensional logistic regression, where the number of covariates is a fraction $ \kappa\in(0,1) $ of the number of observations $ n $, as $ n\to\infty $. We construct a rescaled estimator with zero asymptotic aggregate bias, and define adjusted $ Z $-statistics and rescaled penalized likelihood ratio statistics that exhibit the typical null asymptotic…

不完全区组设计的设计型因果推断AI 解读

Summary Researchers often turn to block randomization to increase the precision of their inference or for practical reasons, such as in multi-site trials. However, if the number of treatments under consideration is large, it may not be feasible or practical to assign all treatments within each block. We develop novel inference results under the finite-population, design-based framework for natural alternatives to the complete block design that do not require reducing the…

用平均导数效应参数化连续处理的影响AI 解读

Summary The average treatment effect is commonly used to quantify the main effect of a binary treatment on an outcome. Extensions to continuous treatments are usually based on the dose-response curve or shift interventions, but both require strong overlap conditions, and the resulting curves may be difficult to summarize. This article focuses instead on average derivative effects, which are scalar estimands related to infinitesimal shift interventions requiring only local…