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近似随机化检验的渐近有效性与有限样本性质AI 解读

Summary Randomization tests rely on simple data transformations and possess an appealing robustness property. In addition to being finite-sample valid if the data distribution is invariant under the transformation, these tests can be asymptotically valid under a suitable studentization of the test statistic, even if the invariance does not hold. However, practical implementation often encounters noisy data, resulting in approximate randomization tests that may not be as…

匹配估计量的自助法一致性AI 解读

Summary In a landmark paper, Abadie & Imbens (2008)showed that the naive bootstrap is inconsistent when applied to nearest neighbour matching estimators of the average treatment effect with a fixed number of matches. Since then, this finding has inspired numerous efforts to address the inconsistency issue, typically by employing alternative bootstrap methods. In contrast, in this work we show that the naive bootstrap is provably consistent for the original matching…

基于特征函数的空间随机性检验AI 解读

Summary We introduce a new type of test for complete spatial randomness that applies to mapped point patterns in a rectangle or a cube of any dimension. This is the first test of its kind to be based on characteristic functions and utilizes a weighted $ L_{2} $ distance between the empirical and uniform characteristic functions. The test shows surprising connections with Ripley’s $ K $-function and Zimmerman’s $ \bar{\omega}^{2} $ statistic. It is also simple to calculate…

稀疏高阶偏最小二乘：变量选择、降维与张量去噪AI 解读

Abstract Motivated by the challenge of estimating effects of DNA methylation on 3D genomic contacts captured by multimodal single-cell Hi-C data, we consider the tensor-response partial least squares model with $ \mathcal{Y}=\mathcal{B}\times_{1}X+\mathcal{F} $, where the correlated high-dimensional predictors $ X\in\mathbb{R}^{n\times d_{1}} $ and the sparse and noisy high-dimensional responses $ \mathcal{Y}\in\mathbb{R}^{n\times\prod_{m}d_{m}} $ are observed, the low-rank…

汇聚集体智慧：利用多来源决策序列的融合学习AI 解读

Summary We introduce an integrative ranking and thresholding framework for fusing evidence from multiple testing procedures. The key innovation is a method that transforms binary testing decisions into compound $ e $-values, enabling the combination of findings across diverse data sources or studies. We demonstrate that our new framework ensures overall false discovery rate control, provided that the individual studies maintain their respective false discovery rate levels.…

Wishart随机矩阵的数据拆分AI 解读

Summary Recent work has explored data thinning, a generalization of sample splitting that involves decomposing a (possibly matrix-valued) random variable into independent components. In the special case of an $ n\times p $ random matrix with independent and identically distributed $ N_{p}(\mu,\Sigma) $ rows, Dharamshi et al. (2026)provided a comprehensive analysis of the settings in which thinning is or is not possible: briefly, if $ \Sigma $ is unknown then one can thin…

线性混合模型中的一致推断AI 解读

Summary We provide finite-sample distribution approximations that are uniform in the parameter for inference in linear mixed models. The focus is on variances and covariances of random effects in cases where existing theory fails because the covariance matrix is nearly or exactly singular and hence near or at the boundary of the parameter set. Quantitative bounds on the differences between the standard normal density and densities of linear combinations of the score function…

函数型数据的极端相关系数AI 解读

Abstract We propose a coefficient that measures extremal dependence in paired samples of functions. It has properties similar to the Pearson correlation, but differs in two significant ways: (i) it is designed to measure dependence between curves; and (ii) it focuses only on extreme curves. The new coefficient is derived within the framework of regular variation in Banach spaces. A consistent estimator is proposed and justified by an asymptotic analysis and a simulation…

非参数加性回归中交互效应的识别与估计AI 解读

Abstract A new formulation of the additive interaction model is introduced. In contrast to existing approaches, the new formulation separates well the joint effects of covariates that cannot be accounted for by individual main effects. The new approach enables correct interpretation of interaction effects by making them orthogonal to the associated main effects in the $ L^{2} $ sense. A new method is developed to estimate the resulting main and interaction effects.…

因果发现后因果效应的选择后推断AI 解读

Summary Algorithms for constraint-based causal discovery select graphical causal models among a space of possible candidates (e.g., all directed acyclic graphs) by executing a sequence of conditional independence tests. These may be used to inform the estimation of causal effects (e.g., average treatment effects) when there is uncertainty about which covariates ought to be adjusted for, or which variables act as confounders versus mediators. However, naively using the data…

协变量偏移下基于加权共形p值的无模型选择性推断AI 解读

Summary This paper introduces novel weighted conformal $ p $-values and methods for model-free selective inference. The problem is as follows: given test units with covariates $ X $ and missing responses $ Y $, how do we select units for which the responses $ Y $ are larger than user-specified values while controlling the proportion of false positives? Can we achieve this without modelling assumptions on the data and without restriction on the model for predicting the…

未知协方差下的高斯信息分解AI 解读

Abstract Common workflows in machine learning and statistics rely on the ability to partition the information in a dataset into independent portions. Recent work has shown that this may be possible even when conventional sample splitting is not, such as when the number of samples, $ n $, is one or when observations are not independent and identically distributed. In the case of multivariate Gaussian data, these alternatives to sample splitting require knowledge of the…

利用外部数据检验随机临床试验中的生物标志物交互疗效AI 解读

Summary In oncology the efficacy of novel therapeutics often differs across patient subgroups, and these variations are difficult to predict during the initial phases of the drug development process. The relation between the power of randomized clinical trials and heterogeneous treatment effects has been discussed by several authors. In particular, false negative results are likely to occur when the treatment effects concentrate in a subpopulation, but the study design did…

可进行精确似然推断的一族环面扩散过程AI 解读

Abstract We provide a class of diffusion processes for continuous time-varying multivariate angular data with explicit transition probability densities, enabling exact likelihood inference. The presented diffusions are time reversible and can be constructed for any prespecified stationary distribution on the torus, including highly multimodal mixtures. We give results on asymptotic likelihood theory, allowing one-sample inference and tests of linear hypotheses for $ k $ group…

近端间接治疗比较AI 解读

Summary We consider the problem of indirect comparison, where a treatment arm of interest is absent by design in one randomized controlled trial, but available in the other. The former is the target trial, and the latter is the source trial. The identifiability of the target population average treatment effect often relies on conditional transportability assumptions. However, it is a common concern whether all relevant effect modifiers are measured and controlled for. We…

局部因果发现中的结构限制：识别目标变量的直接原因AI 解读

Summary We consider the problem of learning a set of direct causes of a target variable from an observational joint distribution. Learning directed acyclic graphs that represent the causal structure is a fundamental problem in science. Several results are known when the full directed acyclic graph is identifiable from the distribution, such as when a nonlinear Gaussian data-generating process is assumed. Here, we are interested only in identifying the direct causes of one…

空间自回归模型的稳健估计AI 解读

This article proposes and studies two Huber-type estimation approaches, namely, the Huber instrumental variable (IV) estimation and the Huber generalized method of moments (GMM) estimation, for a spatial autoregressive model. We establish the consistency, asymptotic distributions, finite sample breakdown points, and influence functions of these estimators. Simulation studies show that compared to the corresponding traditional estimators (the two-stage least squares…

函数系数回归与气候敏感性分析的新渐近理论AI 解读

A general asymptotic theory is established for sample cross moments of nonstationary time series, allowing for long-range dependence and local unit roots. The theory provides a substantial extension of earlier results on nonparametric regression that include near-cointegrated nonparametric regression as well as spurious nonparametric regression. Many new models are covered by the limit theory, among which are functional coefficient regressions in which both regressors and…

Abstract We introduce an algorithm that simplifies the construction of efficient estimators, making them accessible to a broader audience. ‘Dimple’ takes as input computer code representing a parameter of interest and outputs an efficient estimator. Unlike standard approaches, it does not require users to derive a functional derivative known as the efficient influence function. Dimple avoids this task by applying automatic differentiation to the statistical functional of…

带动态协变量的统计排序AI 解读

Abstract We introduce a general covariate-assisted statistical ranking model within the Plackett–Luce framework. Unlike previous studies that focus on individual effects with fixed covariates, our model allows covariates to vary across comparisons. This added flexibility enhances model fitting but also brings significant challenges in analysis. This article addresses these challenges in the context of maximum likelihood estimation (MLE). We first provide necessary and…

利用计算几何联系进行在线多元变点检测AI 解读

Abstract The increasing volume of data streams poses significant computational challenges for detecting changepoints online. Likelihood-based methods are effective, but a naive sequential implementation becomes impractical online due to high computational costs. We develop an online algorithm that exactly calculates the likelihood ratio test for a single changepoint in p-dimensional data streams by leveraging a fascinating connection with computational geometry. This…

含时变协变量的删失分位数回归AI 解读

Abstract We propose a new class of censored quantile regression models with time-dependent covariates for right-censored failure time data. While time-dependent covariates naturally arise in time-to-event analysis, existing works in the literature discuss treatments for data collected either under an independent censoring mechanism or a longitudinal setting. Our formulation extends the current scope so that the conventional setting of time-dependent covariates can be…

连续处理后变量的主层分析：非参数识别与半参数估计AI 解读

Abstract Post-treatment variables often complicate causal inference. They appear in many scientific problems, including non-compliance, truncation by death, mediation, and surrogate endpoint evaluation. Principal stratification is a strategy to address these challenges by adjusting for the potential values of the post-treatment variables, defined as the principal strata. It allows for characterizing treatment effect heterogeneity across principal strata and unveiling the…

利用生成式神经网络进行双重稳健条件独立检验AI 解读

Abstract This article addresses the problem of testing the conditional independence of two generic random vectors X and Y given a third random vector Z, which plays an important role in statistical and machine learning applications. We propose a new non-parametric testing procedure that avoids explicitly estimating any conditional distributions but instead requires sampling from the two marginal conditional distributions of X given Z and Y given Z. We further propose using a…