论文检索

朱福康与郭祥宇对《流形假说的统计探索》的讨论AI 解读

We congratulate the authors on their introducing theoretical explanations and applications of the manifold hypothesis in statistics based on the latent metric space model. Using the continuity of the mean correlation kernel in the latent metric space model, the authors employ a feature map to connect the metric space of latent variables to a Euclidean space. This links the geometric and statistical properties of the model. Furthermore, the smoothness of the mean correlation…

Sanna Passino 与 Heard 对《流形假说的统计探索》的讨论AI 解读

We congratulate the authors on their excellent contribution and the thoughtful perspectives they brought to this topic. In this contribution, we focus on the assumption of independence between the latent variables Z1,…,Zn in the Latent Metric Space (LMS) model in Section 2. Data often arrive sequentially, leading to intrinsic row-wise dependencies between data matrices observed at T>1 time points, Yt=(Yi,j,t)1≤i≤n,1≤j≤p∈Rn×p,t=1,…,T⁠. This scenario is commonly encountered…

Stehlík 与 Schlather 对《流形假设的统计探索》的评论AI 解读

We congratulate the authors of Whiteley et al. (2025) for opening up a challenging world of exploration of the manifold hypothesis. We fully agree that principal component analysis (PCA) is one the most important tool data analysis that the underlying spectral representation needs generalization. An alternative to a purely statistical modelling is to try to include as much knowledge as possible into the generalization. Schlather and Reinbott (2021) suggest an algebraic…

Bordino 与 Klopp 对《流形假说的统计探索》的讨论AI 解读

We congratulate the authors on an insightful, practice-grounded contribution showing how manifold-like structure can arise naturally from a broad latent metric space (LMS) model. We especially commend the ‘practice-first’ perspective: beginning with pipelines common among practitioners (e.g. PCA to moderate dimension, then t-SNE/UMAP), and then developing theory that explains why such workflows succeed. We shall now comment on a point where practitioners may require further…

加性效应辅助学习AI 解读

Abstract It is quite popular nowadays for researchers and data analysts holding different datasets to seek assistance from each other to enhance their modelling performance. We consider a scenario where different learners hold datasets with potentially distinct variables, and their observations can be aligned by a nonprivate identifier. Their collaboration faces the following difficulties: first, learners may need to keep data values or even variable names undisclosed due…

电子健康记录依赖知识图谱的统计推断AI 解读

Abstract The effective analysis of high-dimensional Electronic Health Record (EHR) data, with substantial potential for healthcare research, presents notable methodological challenges. Employing predictive modeling guided by a knowledge graph (KG), which enables efficient feature selection, can enhance both statistical efficiency and interpretability. While various methods have emerged for constructing KGs, existing techniques often lack statistical certainty concerning the…

人工编辑下大语言模型水印的稳健检测AI 解读

Abstract Watermarking is an effective approach to distinguishing text generated by large language models (LLMs) from human-written text. However, the pervasive presence of human edits on LLM-generated text dilutes watermark signals, thereby significantly degrading detection performance of existing methods. In this paper, by modelling human edits through mixture model detection, we introduce a new method—a truncated goodness-of-fit test (Tr-GoF) for detecting watermarked text…

基于分块极大值估计量的自助法AI 解读

Abstract The block maxima method is a standard approach for analyzing the extremal behaviour of a potentially multivariate time series. It has recently been found that the classical approach based on disjoint block maxima may be universally improved by considering sliding block maxima instead. However, the asymptotic variance formula for estimators based on sliding block maxima involves an integral over the covariance of a certain family of multivariate extreme value…

流形上热核高斯过程的可扩展贝叶斯推断AI 解读

Abstract We establish a scalable manifold learning method and theory, motivated by the problem of estimating functional magnetic resonance imaging activation manifolds in the Human Connectome Project. Our primary contribution is the development of an efficient estimation technique for heat kernel Gaussian processes in the exponential family model. This approach handles large sample sizes n, preserves the intrinsic geometry of data, and significantly reduces computational…

黎曼流形上的 Huber 均值AI 解读

Abstract This article introduces Huber means on Riemannian manifolds, providing a robust alternative to the Fréchet mean by integrating elements of both L2 and L1 loss functions. The Huber means are designed to be highly resistant to outliers while maintaining efficiency, making it a valuable generalization of Huber’s M-estimator for manifold-valued data. We comprehensively investigate the statistical and computational aspects of Huber means, demonstrating their utility in…

正则化广义协方差估计量AI 解读

Summary This paper proposes the Regularized Generalized Covariance (RGCov) estimator, a ridge-type extension of the Generalized Covariance (GCov) estimator for high-dimensional stationary time series. By regularizing the GCov objective function, which involves the inverse of the covariance matrix, RGCov improves numerical stability while preserving positive definiteness. Under suitable conditions, the new estimator is consistent, asymptotically normal, and…

用于跳跃检测的随机化检验AI 解读

This article proposes sequential randomized tests to locate the presence of jumps on the paths of efficient asset prices in a continuous-time model. The randomized statistics are generated by artificially adding randomness to the robust approximations of the locally averaged returns of the efficient price. In the case of finite activity jumps, we derive the asymptotic distribution of the maximum of all the local statistics unaffected by jumps, which makes it feasible to…

基于深度分位数过程回归的分布性离策略评估AI 解读

This paper investigates the off-policy evaluation (OPE) problem from a distributional perspective. Rather than focusing solely on the expectation of the total return, as in most existing OPE methods, we aim to estimate the entire return distribution. To this end, we introduce a quantile-based approach for OPE using deep quantile process regression, presenting a novel algorithm called Deep Quantile Process regression-based Off-Policy Evaluation (DQPOPE). We provide new…

深度 Cox 模型的小批量估计：统计基础与实践指导AI 解读

The stochastic gradient descent (SGD) algorithm has been widely used to optimize deep Cox neural network (Cox-NN) by updating model parameters using mini-batches of data. We show that SGD aims to optimize the average of mini-batch partial-likelihood, which is different from the standard partial-likelihood. This distinction requires developing new statistical properties for the global optimizer, namely, the mini-batch maximum partial-likelihood estimator (mb-MPLE). We…

网络回归与监督式中心性估计AI 解读

The centrality in a network is often used to measure nodes’ importance and model network effects on a certain outcome. Empirical studies widely adopt a two-stage procedure, which first estimates the centrality from the observed noisy network and then infers the network effect from the estimated centrality, even though it lacks theoretical understanding. We propose a unified modeling framework to study the properties of centrality estimation and inference and the subsequent…

观察性研究中用于因果推断的人工智能贝叶斯生成模型AI 解读

Causal inference in observational studies with high-dimensional covariates presents significant challenges. We introduce CausalBGM, an AI-powered Bayesian generative modeling approach that captures the causal relationship among covariates, treatment, and outcome. The core innovation is to estimate the individual treatment effect (ITE) by learning the individual-specific distribution of a low-dimensional latent feature set (e.g., latent confounders) that drives changes in…

统计研究的新兴知识趋势：协变量辅助动态主题模型分析AI 解读

Exploring the emerging knowledge trends within a particular discipline is of great interest to scientific researchers. It helps researchers to understand the historical development of their disciplines and to guide their future research directions. In this work, we focus on the fast-developing discipline, statistics, to investigate its knowledge trend and emerging topics in statistical research. To this end, we collect publications in top-tier statistical journals and…

人机交互的收益：混杂环境中的超级策略学习AI 解读

As AI becomes more prevalent throughout society, effective methods of integrating humans and AI systems that leverage their respective strengths and mitigate risk have become an important priority. In this article, we introduce the paradigm of super policy learning that takes advantage of Human-AI interaction for data driven sequential decision making. This approach uses the observed action, either from AI or humans, as input for achieving a stronger oracle in policy…

基于汇总数据、消除赢家诅咒的稳健孟德尔随机化AI 解读

In the past decade, the increased availability of genome-wide association studies summary data has popularized Mendelian Randomization (MR) for conducting causal inference. MR analyses, incorporating genetic variants as instrumental variables, are known for their robustness against reverse causation bias and unmeasured confounders. Nevertheless, classical MR analyses using summary data may still produce biased causal effect estimates due to the winner's curse and pleiotropy…

工作稳定性对意大利货币贫困的影响：因果小区域估计AI 解读

Job stability – encompassing secure contracts, adequate wages, social benefits, and career opportunities – is a critical determinant in reducing monetary poverty, as it provides households with reliable income and enhances economic well-being. This study draws on EU-SILC survey and census data to estimate the causal effect of job stability on monetary poverty across Italian provinces, quantifying its influence, and analyzing regional disparities. We introduce a novel causal…

局部平稳过程的贝叶斯非参数谱分析AI 解读

Stationarity plays a pivotal role in time series analysis. It is not only the basis for the derivation of general asymptotic theory but it also allows an efficient analysis in the frequency domain via the Whittle likelihood, based on the asymptotic independence of the Fourier coefficients. However, many regularly sampled data derived from the observation of physical or ecological processes, for instance, are only locally stationary. They exhibit slowly evolving spectra and…

高维条件因子模型估计的统一框架AI 解读

This article presents a general framework for estimating high-dimensional conditional latent factor models via constrained nuclear norm regularization. We establish large sample properties of the estimators and provide efficient algorithms for their computation. To improve practical applicability, we propose a cross-validation procedure for selecting the regularization parameter. Our framework unifies the estimation of various conditional factor models, enabling the…

多重矩等式检验的功效增强：超越二范数与无穷范数AI 解读

Tests based on the 2- and ∞-norm have received considerable attention in high-dimensional testing problems, as they are powerful against dense and sparse alternatives, respectively. The power enhancement principle of Fan et al. (2015) combines these two norms to construct improved tests that are powerful against both types of alternatives. In the context of testing whether a candidate parameter satisfies a large number of moment equalities, we construct tests that harness…

基于条件尖峰—厚尾先验的协变量细化稳健部分信息迁移AI 解读

The popularity of transfer learning stems from the fact that it can borrow information from useful auxiliary datasets. Existing statistical transfer learning methods usually adopt a global similarity measure between the source data and the target data, which may lead to inefficiency when only partial information is shared. In this paper, we propose a novel Bayesian transfer learning method named “CONCERT” to allow robust partial information transfer for high-dimensional data…