Journal of the Royal Statistical Society: Series B

最新文章

• Authors' reply to the Discussion of 'Augmented balancing weights as linear regression' by Bruns-Smith et al. AI翻译
  作者：Bruns-Smith, David; Dukes, Oliver; Feller, Avi; Ogburn, Elizabeth L.
  作者单位：Stanford University; Ghent University; University of California System; University of California Berkeley; University of California System; University of California Berkeley; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health
• Semiparametric fiducial inference for Cox models AI翻译
  作者：Cui, Yifan; Hannig, Jan; Edlefsen, Paul
  作者单位：Zhejiang University; University of North Carolina; University of North Carolina Chapel Hill; Fred Hutchinson Cancer Center
  摘要：R.A. Fisher introduced the fiducial distribution as a potential replacement for the Bayesian posterior distribution in the 1930s. During the past century, fiducial approaches have been explored in various parametric and nonparametric settings. However, to the best of our knowledge, no fiducial inference has been developed in the realm of semiparametric statistics. In this paper, we propose a novel fiducial approach for semiparametric models. In memory of Sir David Cox, who passed away in 2022,...
• Charles R. Doss and Jared D. Huling's contribution to the Discussion of 'Augmented balancing weights as linear regression' by Bruns-Smith et al. AI翻译
  作者：Doss, Charles R.; Huling, Jared D.
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; University of Minnesota System; University of Minnesota Twin Cities
  摘要：Doss and Huling's contribution to the Discussion of 'Augmented balancing weights as linear regression' by Bruns-Smith et al.
• Causal inference under uniformly bounded neighbourhood interference AI翻译
  作者：Lu, Xin; Li, Hongzi; Liu, Hanzhong
  作者单位：Tsinghua University
  摘要：Randomized experiments remain the gold standard for estimating treatment effects; however, network interference compromises the validity of traditional estimators by violating the stable unit treatment value assumption and introducing bias. Although cluster-randomized designs help mitigate some bias, they struggle to accommodate complex network structures and cannot disentangle direct from indirect effects. To address these challenges, we develop a design-based asymptotic theory for Horvitz-Th...
• Parametric and nonparametric symmetries in graphical models for extremes AI翻译
  作者：Rottger, Frank; Coons, Jane Ivy; Grosdos, Alexandros
  作者单位：University of Twente; Worcester Polytechnic Institute; University of Augsburg
  摘要：Coloured graphical models provide a parsimonious approach to modeling high-dimensional data by exploiting symmetries in the model parameters. In this work, we introduce the notion of colouring for extremal graphical models on generalized multivariate Pareto distributions, a natural class of limiting distributions for threshold exceedances. Thanks to a stability property of the generalized multivariate Pareto distributions, coloured extremal tree models can be defined fully nonparametrically. F...
• Statistical exploration of the manifold hypothesis AI翻译
  作者：Agterberg, Joshua
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign
  摘要：The manifold hypothesis is a widely accepted tenet of machine learning which asserts that nominally high-dimensional data are in fact concentrated near a low-dimensional manifold, embedded in high-dimensional space. This phenomenon is observed empirically in many real-world situations, has led to development of a wide range of statistical methods in the last few decades, and has been suggested as a key factor in the success of modern AI technologies. We show that rich and sometimes intricate m...
• Andrew Gelman's contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al. AI翻译
  作者：Gelman, Andrew
  作者单位：Columbia University; Columbia University
• Spatial effect detection regression for large-scale spatio-temporal covariates (vol 87, pg 872, 2025) AI翻译
  作者：Zhang, Chenlin; Zhou, Ling; Guo, Bin; Lin, Huazhen
• Inference of dependency knowledge graph for Electronic Health Records AI翻译
  作者：Xu, Zhiwei; Gan, Ziming; Zhou, Doudou; Shen, Shuting; Lu, Junwei; Cai, Tianxi
  作者单位：University of Michigan System; University of Michigan; University of Chicago; National University of Singapore; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard Medical School
  摘要：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 presence of links between en...
• Multilayer random dot product graphs: estimation and online change point detection1 AI翻译
  作者：Wang, Fan; Li, Wanshan; Madrid Padilla, Oscar Hernan; Yu, Yi; Rinaldo, Alessandro
  作者单位：University of Warwick; University of California System; University of California Los Angeles; University of Texas System; University of Texas Austin
  摘要：We study the multilayer random dot product graph (MRDPG) model, a generalization of the random dot product graph model to multilayer networks. To estimate the edge probabilities, we deploy a tensor-based methodology and demonstrate its superiority over existing approaches. Moving to dynamic MRDPGs, we formulate and analyse an online change point detection framework, where, at each time point, we observe a realization from an MRDPG. Across layers, we assume fixed shared common node sets and lat...