论文检索

摘要暂缺

摘要暂缺

检验阴性设计的敏感性分析AI 解读

The test-negative design has become popular for evaluating the effectiveness of post-licensure vaccines using observational data. In addition to its logistical convenience on data collection, the design is also believed to control for the differential health-care-seeking behavior between vaccinated and unvaccinated individuals, an important while often unmeasured confounder between the vaccination and infection. Hence, the design has been employed routinely to monitor…

预印本版本摘要 · 查看来源

二元数据二次分配程序的渐近理论AI 解读

Abstract The quadratic assignment procedure (QAP) is a popular tool for analysing dyadic data in medical and social sciences. To test the association between 2 dyadic measurements represented by 2 symmetric matrices, QAP calculates the p-value by permuting the units, or equivalently, by permuting the rows and columns of 1 matrix in the same way. Its extension to the regression setting, known as the multiple regression QAP, has also gained popularity, especially in…

Bures-Wasserstein 流形上 Fréchet 回归的偏效应检验AI 解读

Abstract We propose a novel test for assessing partial effects in Fréchet regression on the Bures-Wasserstein manifold. Under the null hypothesis, we show that the statistic admits a degenerate V-statistic approximation whose limiting distribution is aweighted mixture of chi-squared random variables, with weights determined by the eigenvalues of an integral operator associated with a reproducing kernel Hilbert space (RKHS) kernel. We establish the asymptotic validity and…

用分数阶导数推导边际密度AI 解读

Abstract This paper presents a novel method for analytical derivations of marginal densities using the fractional derivatives of moment-generating functions. Although the method requires likelihood functions to take specific forms, its assumptions are otherwise modest. It only requires that the prior moment-generating functions exist, are finite, and are continuous and differentiable at certain points. We also present the probabilistic and statistical insights behind this…

条件双样本检验的一般框架AI 解读

Abstract We study the problem of conditional two-sample testing, which aims to determine whether two populations have the same distribution after accounting for confounding factors. This problem commonly arises in various applications, such as domain adaptation and algorithmic fairness, where comparing two groups is essential while controlling for confounding variables. We begin by establishing a hardness result for conditional two-sample testing, demonstrating that no valid…

摘要暂缺

摘要暂缺

超越传统旋转：无偏稀疏表示学习与 oracle 推断AI 解读

Abstract Learning low-dimensional latent representations is a central topic in statistics and machine learning, and rotation methods have long been used to obtain sparse and interpretable representations. Despite nearly a century of widespread use across many fields, rigorous guarantees for valid inference for the learned representation remain lacking. In this paper, we identify a surprisingly prevalent phenomenon that suggests a reason for this gap: for a broad class of…

时变面板数据模型中的潜在组结构估计AI 解读

Abstract We consider panel data models where coefficients change smoothly over time and follow a latent group structure, being homogeneous within but heterogeneous across groups. To jointly estimate the group memberships and group-specific coefficient trajectories, we propose FUSE-TIME, a pairwise adaptive group fused-Lasso estimator combined with polynomial spline sieves. We establish consistency, derive the asymptotic distributions of the penalized sieve estimator and its…

基于因子模型的时变自动对齐重叠聚类AI 解读

In many scientific fields such as genomics and neuroscience, data often exhibit complex structures where features can belong to multiple clusters simultaneously and vary over time. Traditional clustering methods such as k-means and hierarchical clustering typically assume static cluster structures and perform hard assignments, limiting their ability to capture the underlying overlapping and time-varying cluster structures. To address these challenges, we propose a novel…

流数据非平稳密度核估计的逐块动态平滑参数选择AI 解读

Abstract We consider nonparametric estimation of the nonstationary density of streaming data collected continuously over time. Those types of data are often available only for a limited time after being observed, for example due to storage or data privacy constraints. Therefore, analyzing them requires dynamic approaches that do not require repeated access to past data. A suitable iterative kernel estimator of nonstationary densities has been developed by Rutkowski (1989);…

多因子生产函数协整系统的估计与推断：GDP 与排放的联合建模AI 解读

Abstract This paper develops integrated modified ordinary and generalized least squares estimation for systems of cointegrating multivariate polynomial regressions, i. e., systems of equations that include deterministic variables, integrated processes and (cross-)products of non-negative integer powers of these variables as regressors. The stationary errors are allowed to be correlated across equations, over time and with the regressors. The necessity to consider integrated…

开放集标签偏移数据的半参数学习AI 解读

Abstract We study the open-set label shift problem, where the test data may include a novel class absent from the training data. This setting is challenging because both the class proportions and the distribution of the novel class are not identifiable without extra assumptions. Existing approaches often rely on restrictive separability conditions, prior knowledge, or computationally infeasible procedures, and some may lack theoretical guarantees. We propose a semiparametric…

用广义似然比检验矩阵因子结构AI 解读

Abstract In this paper, we introduce a generalized likelihood ratio test for assessing whether the factor loading matrix in high-dimensional data exhibits a Kronecker structure, thereby offering a rigorous tool for validating two-way matrix factor models. A central challenge in likelihood-based inference for factor models arises from inherent rotational non-identifiability, which prevents the likelihood function from admitting a standard second-order Taylor expansion. To…

含生成回归元的半参数模型自助设定检验AI 解读

This article provides a specification test for semiparametric models with nonparametrically generated regressors. Such regressors are not observed by the researcher but are nonparametrically identified and estimable. Applications of the test include models with endogenous regressors identified by control functions, semiparametric sample selection models, or binary games with incomplete information. The statistic is built from the residuals of the semiparametric model. A…

何时不应将线性IV估计量解释为LATE？AI 解读

Abstract In this paper I revisit the interpretation of the linear instrumental variables (IV) estimand as a weighted average of conditional local average treatment effects (LATEs). I focus on a situation in which additional covariates are required for identification while the reduced-form and first-stage regressions may be misspecified due to an implicit homogeneity restriction on the effects of the instrument. I show that the weights on some conditional LATEs are negative…

高维计数数据的半监督表示学习聚类AI 解读

High-dimensional count data arise in many modern applications, such as electronic health records (EHR) and RNA sequencing, with sparse, heterogeneous observations driven by complex latent structure. These characteristics make modeling and prediction challenging, especially when labels are limited. We propose SCORE, a semi-supervised representation learning framework for clustering and embedding construction from high-dimensional count features. SCORE is built on a…

Perturb-seq 中基于代理变量与工具变量的混杂稳健因果发现与推断AI 解读

Emerging single-cell technologies that combine CRISPR-based genetic perturbations with single-cell RNA sequencing, such as Perturb-seq, offer unprecedented opportunities to uncover cause-and-effect relationships among genes. Nonetheless, Perturb-seq experiments are subject to unobserved factors that, if not properly handled, can severely bias the inferred causal relationships between genes. These latent factors may arise not only from intrinsic molecular features of the…

预印本版本摘要 · 查看来源

面向强超高斯似然的广义切平面近似变分推断框架AI 解读

Variational inference, as an alternative to Markov chain Monte Carlo sampling, has played a transformative role in enabling scalable computation for complex Bayesian models. Nevertheless, existing approaches often depend on either rigid model-specific formulations or stochastic black-box optimization routines. Tangent approximation is a principled class of structured variational methods that exploits the geometry of the underlying probability model. However, its utility has…

摘要暂缺

可加全局Fréchet回归的变量选择AI 解读

We present a novel framework for variable selection in Fréchet regression with responses in general metric spaces, a setting increasingly relevant for analyzing non-Euclidean data such as probability distributions and covariance matrices. Building on the concept of (weak) Fréchet conditional means, we develop an additive regression model that represents the metric-based discrepancy of the response as a sum of covariate-specific nonlinear functions in reproducing kernel…

预印本版本摘要 · 查看来源

度量空间中多元随机过程的幅度与相位变异建模AI 解读

In functional data analysis, distinguishing between amplitude and phase variation is essential to prevent distorted statistical inferences. While extensive work has been devoted to developing time-warping methods to address this issue for Euclidean functional data, much less is known about how to characterize and model these two sources of variability when they co-exist in random processes taking values in general metric spaces. In this paper, we develop a comprehensive…