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作者：Donoho, David L.; Kang, Jian; Lin, Xihong; Mukherjee, Bhramar; Nettleton, Dan; Nugent, Rebecca; Rodriguez, Abel; Xing, Eric P.; Zheng, Tian; Zhu, Hongtu
作者单位：Stanford University; University of Michigan System; University of Michigan; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard University; Massachusetts Institute of Technology (MIT); Broad Institute; Yale University; Yale University; Iowa State University; Carnegie Mellon University; University of California System; University of California Santa Cruz; Mohamed bin Zayed University of Artificial Intelligence MBZUAI; Carnegie Mellon University; Columbia University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
摘要：This perspective article distills themes from the 2024 JSM town hall, Statistics in the Age of AI, where panelists discussed how the field should evolve as foundation models, large-scale empirical modeling, and data-intensive infrastructures reshape science and society. Rather than a transcript or comprehensive survey, the article offers a forward-looking perspective grounded in the town hall discussion and the authors' reflections. We organize the discussion around five recurring questions: (...
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作者：Liu, Xu; Chan, Kin Wai
作者单位：Chinese University of Hong Kong
摘要：Prewhitening is a common approach to deal with strong autocorrelation. In this article, we propose a new approach called tail postcoloring, motivated by it. It uses parametric models to project, or color back, the neglected tail autocovariances in nonparametric estimators onto the final estimator. This approach bridges the nonparametric variance estimator and the parametric coloring model through a scaling factor. It automatically switches between these two arms using a bandwidth parameter, wi...
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作者：Chang, Jinyuan; Yang, Lin; Zha, Mengyue; Zhou, Wen-Xin
作者单位：Southwestern University of Finance & Economics - China; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Peking University; Hong Kong University of Science & Technology; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital
摘要：While the traditional goal of statistics is to infer population parameters, modern practice increasingly demands protection of individual privacy. One way to address this need is to adapt classical statistical procedures into privacy-preserving algorithms. In this article, we develop differentially private tail-robust methods for linear regression. The tradeoff among bias, privacy, and robustness is controlled by a tunable robustification parameter in the Huber loss. We implement noisy clipped...
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作者：Zhang, Yijiao; Yuan, Yubai; Zhang, Yuexia; Zhu, Zhongyi; Qu, Annie
作者单位：University of Pennsylvania; Pennsylvania Medicine; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of Texas System; University of Texas at San Antonio; Fudan University; University of California System; University of California Santa Barbara
摘要：Mediation analysis plays a crucial role in causal inference as it can investigate the pathways through which treatment influences outcome. Most existing mediation analysis assumes that mediation effects are static and homogeneous within populations. However, mediation effects usually change over time and exhibit significant heterogeneity among individuals in many real-world applications. Additionally, the mediation mechanism can be complicated and involves non-sparse, making mediator selection...
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作者：Bortolato, Elena; Canale, Antonio
作者单位：Pompeu Fabra University; Barcelona School of Economics; University of Padua
摘要：Factor Analysis has traditionally been used across diverse disciplines to extrapolate latent traits that influence the behavior of multivariate observed variables. Historically, the focus has been on analyzing data from a single study, neglecting the potential study-specific variations present in data from multiple studies. Multi-study factor analysis has emerged as a recent methodological advancement that addresses this gap by distinguishing between latent traits shared across studies and stu...
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作者：Jia, Junxiong; Meng, Deyu; Xu, Zongben; Yao, Fang
作者单位：Xi'an Jiaotong University; Pazhou Lab; Peking University
摘要：This article addresses Bayesian inference related to partial differential equations (PDEs), particularly nonparametric regression constrained by PDEs. To effectively encode prior information, we propose a novel framework that learns a prediction function of the prior distribution from historical training datasets. We introduce hyper-prior and hyper-posterior distributions and derive a generalization error estimate, which accommodates data-dependent priors by extending the concept of differenti...
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作者：Liu, Wei; Lin, Huazhen; Zheng, Shurong; Liu, Jin
作者单位：Sichuan University; Southwestern University of Finance & Economics - China; Southwestern University of Finance & Economics - China; Northeast Normal University - China; The Chinese University of Hong Kong, Shenzhen
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作者：Zou, James; Yuksekgonul, Mert
作者单位：Stanford University
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作者：Han, Yang; Wu, Weichi; Zhang, Wenyang
作者单位：University of Manchester; Tsinghua University
摘要：In panel data analysis, individual attributes are of importance in many real applications. With the advancement of data collection, it is often possible to acquire enough information for individual attributes in a collected panel dataset, and data from other individuals may contain the information for the attributes of the individual under concern. Homogeneity pursuit is an important topic in panel data analysis when individual attributes are of interest. Existing approaches are mainly based o...
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作者：Shen, Shuting; Lu, Junwei; Lin, Xihong
作者单位：National University of Singapore; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University
摘要：In light of the rapidly growing large-scale data in federated ecosystems, the traditional principal component analysis (PCA) is often not applicable due to privacy protection considerations and large computational burden. Algorithms were proposed to lower the computational cost, but few can handle both high dimensionality and massive sample size under distributed settings. In this article, we propose the FAst DIstributed (FADI) PCA method for federated data when both the dimension d and the sa...
