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重尾与污染下基于核的自适应Huber均值回归AI 解读

We consider robust conditional mean learning in the mathematical framework of reproducing kernel Hilbert spaces, assuming the errors can be heavy-tailed and at the same time some responses are subject to arbitrary contamination. Simultaneous robustness to both heavy tails and contamination are of interest. When there is no contamination, we establish rates when the (1+θ)-moment ( θ>0) of the error exists, which matches the minimax rate for the least squares regression as…

用于有序回归的延续比Logit结构化混合模型AI 解读

We develop a nonparametric Bayesian modeling approach to ordinal regression based on priors placed directly on the discrete distribution of the ordinal responses. The prior probability models are built from a structured mixture of multinomial distributions. We leverage the continuation-ratio logits representation to formulate the mixture kernel, with mixture weights defined through the logit stick-breaking process that incorporates the covariates through a linear function.…

公平性感知的高斯图回归及脑共表达QTL研究应用AI 解读

Glioblastoma multiforme (GBM) is a highly aggressive brain cancer with largely ineffective treatment. It is imperative to explore more effective therapies, such as gene-based treatments. For co-expression QTL studies of GBM, we develop fairness-aware Gaussian graphical regression models (Fair RegGGMs), which can determine how genetic variants modulate subject-level gene networks, and recover both population-level and subject-level gene graphs, while ensuring that the…

树结构大规模稀疏线性方程组及其在光纤网络中的应用AI 解读

Fiber optic networks require precise time synchronization, yet asymmetric edges introduce delays that challenge accurate correction. Current solutions like manual detection or the deployment of Global Navigation Satellite System (GNSS) receivers are impractical due to high costs and labor requirements. Statistically, delay detection is complicated by non-identifiability, fixed tree topologies, and absent structured sparsity assumptions. To overcome these difficulties, we…

自适应采集数据的随机化检验AI 解读

Randomization testing is a fundamental method in statistics, enabling inferential tasks such as testing for (conditional) independence of random variables, constructing confidence intervals in semiparametric location models, and constructing (by inverting a permutation test) model-free prediction intervals via conformal prediction. Randomization tests are exactly valid for any sample size, but their use is generally confined to exchangeable data. Yet in many applications,…

异质站点下单轮通信的分布式半参数推断AI 解读

Distributed inference has proven useful in contexts where data privacy is the central concern. However, privacy constraints often necessitate cumbersome coordination to exchange summary data during distributed learning. Most existing methods require two rounds of data communication, yet excessive communication rounds might discourage participation from local sites. This study develops distributed algorithms that require only a single communication round while maintaining…

中介分析中复合原假设的最优检验AI 解读

The indirect effect of an exposure on an outcome through an intermediate variable can be identified by a product of two regression coefficients under certain causal and regression modeling assumptions. In this context, the null hypothesis of no indirect effect is a composite null hypothesis, as the null holds if either regression coefficient is zero. A consequence is that traditional hypothesis tests are severely underpowered near the origin (i.e., when both coefficients are…

通过分而治之实现可扩展的时间序列贝叶斯推断AI 解读

Bayesian computation often scales poorly with increasing data size, motivating developments such as divide-and-conquer approaches for scalable inference. These methods partition the data into subsets, perform parallel inference on each subset, and aggregate the results into a single posterior. Appealing theoretical properties and practical performance have been demonstrated for independent data; however, methods for dependent data remain challenging. Existing methods rely on…

通过广义 Dantzig 选择器构建可解释的标量对图像线性回归AI 解读

The scalar-on-image regression model examines the association between a scalar response and a bivariate function (e.g., images) through the estimation of a bivariate coefficient function. Existing approaches often impose smoothness constraints to control the bias-variance trade-off, and thus prevent overfitting. However, such assumptions can hinder interpretability, especially when only certain regions of an image influence changes in the response. In such a scenario,…

生成式人工智能辅助因果推断：以文本作为处理AI 解读

In this paper, we demonstrate how to enhance the validity of causal inference with unstructured high-dimensional treatments like texts, by leveraging the power of generative Artificial Intelligence (GenAI). Specifically, we propose to use a deep generative model such as large language models (LLMs) to efficiently generate treatments and use their internal representation for subsequent causal effect estimation. We show that the knowledge of this true internal representation…

广义潜在因子模型的可识别性与推断AI 解读

Generalized latent factor analysis not only provides a useful latent embedding approach in statistics and machine learning, but also serves as a widely used tool across various scientific fields, such as psychometrics, econometrics, and social sciences. Ensuring the identifiability of latent factors and the loading matrix is essential for the model’s estimability and interpretability, and various identifiability conditions have been employed by practitioners. However,…

利用不完美辅助信息增强局部共形预测AI 解读

There is growing interest in constructing conformal prediction sets that provide approximate or asymptotic conditional coverage guarantees, capturing local data heterogeneity. However, methods like localized conformal prediction (LCP) may face challenges in ensuring reliable prediction sets in regions with sparse calibration data. This paper introduces Enhanced Localized Conformal Prediction (ELCP), a novel approach that incorporates auxiliary data to refine localized…

通过剖面化迁移学习消除混杂AI 解读

Unmeasured confounders are a major source of bias in regression-based effect estimation and causal inference. In this paper, we propose a new profiled transfer learning framework, ProTrans, to address confounding effects in the target dataset, when additional source datasets with similar confounding structures are available. We introduce the concept of profiled residuals to characterize the shared confounding patterns between source and target datasets. By incorporating…

函数型数据变化率的在线学习AI 解读

With advances in modern data collection technologies, functional data are increasingly received in a streaming manner. Although numerous methods have been developed to model such data, most focus on estimating the mean and covariance functions, while giving limited attention to their derivatives. In this paper, we introduce an online method for estimating the change rates of the mean and covariance functions, enabling real-time updates with high statistical efficiency. The…

高维数据整合中的最优网络引导协变量选择AI 解读

Modern data often arise with multiple modalities. For example, covariates and a network are observed on the same subjects, and both contain useful information. Effectively integrating these modalities is important and challenging, especially when the response is unavailable. We study the fundamental covariate selection problem for high-dimensional data by leveraging network information.We propose the Network-Guided Covariate Selection (NGCS) algorithm. NGCS exploits the…

高维时间序列因子模型的权重校准估计AI 解读

The factor modeling for high-dimensional time series is powerful in discovering latent common components for dimension reduction and information extraction. Most available estimation methods can be divided into two categories: the covariance-based under asymptotically-identifiable assumption and the autocovariance-based with white idiosyncratic noise. This paper follows the autocovariance-based framework and develops a novel weight-calibrated method to improve the estimation…

用户级局部差分隐私的速率最优性与相变AI 解读

Most literature on differential privacy considers the item-level case where each user has a single observation, but a growing field is that of user-level privacy where each of the n users holds T observations and wishes to maintain the privacy of their entire collection. We derive a general minimax lower bound, which shows that, for locally private user-level estimation problems, the risk cannot, in general, be made to vanish for a fixed n even for T arbitrarily large. We…

CUSUM 停止时，有何统计依据认为警报并非误报？AI 解读

In the framework of the Cusum procedure, the evolution of a false alarm has a well-understood stochastic behavior. So, if observations preceding an alarm were to exhibit a behavior that is significantly different, there would be reason to reject the hypothesis that the alarm is false. We develop a test of this difference. The method is applied to detecting a change in a Covid-19 context involving a possible increase of a mean and in a context involving a possible increase in…

高斯不变马尔可夫链蒙特卡洛AI 解读

We develop sampling methods, which consist of Gaussian invariant versions of random walk Metropolis (RWM), Metropolis adjusted Langevin algorithm (MALA) and second order Hessian or Manifold MALA. Unlike standard RWM and MALA, we show that Gaussian invariant sampling can lead to ergodic estimators with improved statistical efficiency. This is due to a remarkable property of Gaussian invariance that allows us to obtain exact analytical solutions to the Poisson equation for…

《空间密度建模：统计、计量经济学与机器学习中的数据、方法及 R 应用》书评条目AI 解读

Katarzyna Kopczewska. New York, NY: Oxford University Press, 2025, xii + 307 pp., $38.99(e-book), ISBN: 978-0-198-97518-2.Modelling Spatial Density: Data, Methods, and R Applications in Statistics ...

数据不共享条件下稀疏高维模型的协同推断AI 解读

In modern data analysis, an improvement in statistical efficiency is expected via effective collaboration among multiple data holders with non-shared data. In this article, we propose a collaborative score-type test (CST) for testing linear hypotheses, which accommodates potentially high-dimensional nuisance parameters and a diverging number of constraints and target parameters. Through a careful decomposition of the Kiefer-Bahadur representation for the traditional score…

通过函数逼近对随机趋势进行典型相关分析AI 解读

This paper proposes a novel approach for semiparametric inference on the number s of common trends and their loading matrix ψ in I(1)/I(0) systems. It combines functional approximation of limits of random walks and canonical correlation analysis, performed between the p observed time series of length T and the first K discretized elements of an L2 basis. Tests and selection criteria for s and estimators and tests on ψ are proposed; their properties are discussed as T and K…

线性模型选择与预测中的分块 g 先验狄利克雷过程混合AI 解读

This paper introduces Dirichlet process mixtures of block g priors for model selection and prediction in linear models. These priors are extensions of traditional mixtures of g priors that allow for differential shrinkage for various (data-selected) blocks of parameters while fully accounting for the predictors’ correlation structure, providing a bridge between the literatures on model selection and continuous shrinkage priors. We show that Dirichlet process mixtures of…

遗传风险评分构建：投影汇总统计与灵活收缩的稳健贝叶斯方法AI 解读

Polygenic risk scores (PRS) developed from genome-wide association studies (GWAS) can be used for risk stratification by quantifying the genetic contribution to disease, and many clinical applications have been proposed. Bayesian methods are popular for building PRS because of their natural ability to regularize models and incorporate external information. In this article, we present new theoretical results, methods, and extensive numerical studies to advance Bayesian…