2026

最新文章

• Simplifying debiased inference via automatic differentiation and probabilistic programming AI翻译
  作者：Luedtke, Alex
  作者单位：University of Washington; University of Washington Seattle
  摘要：We introduce an algorithm that simplifies the construction of efficient estimators, making them accessible to a broader audience. 'Dimple' takes as input computer code representing a parameter of interest and outputs an efficient estimator. Unlike standard approaches, it does not require users to derive a functional derivative known as the efficient influence function. Dimple avoids this task by applying automatic differentiation to the statistical functional of interest. Doing so requires exp...
• Inference of dependency knowledge graph for Electronic Health Records (Sep, qkaf061, 2025) AI翻译
  作者：Xu, Zhiwei; Gan, Ziming; Zhou, Doudou; Shen, Shuting; Lu, Junwei; Cai, Tianxi
• Representation of context-specific causal models with observational and interventional data AI翻译
  作者：Duarte, Eliana; Solus, Liam
  作者单位：Universidade do Porto; Royal Institute of Technology
  摘要：We address the problem of representing context-specific causal models based on both observational and experimental data collected under general (e.g. hard or soft) interventions by introducing a new family of context-specific conditional independence models called CStrees. This family is defined via a novel factorization criterion that allows for a generalization of the factorization property defining general interventional directed acyclic graph (DAG) models. We derive a graphical characteriz...
• Bootstrapping estimators based on the block maxima method AI翻译
  作者：Bucher, Axel; Staud, Torben
  作者单位：Ruhr University Bochum
  摘要：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 distributions, which makes its esti...
• Fully decentralized inference for spatial data using low-rank models AI翻译
  作者：Shi, Jianwei; Abdulah, Sameh; Sun, Ying; Genton, Marc G.
  作者单位：King Abdullah University of Science & Technology; King Abdullah University of Science & Technology
  摘要：Advancements in information technology have enabled the creation of massive spatial datasets, driving the need for scalable and efficient computational methodologies. Although offering viable solutions, centralized frameworks are limited by vulnerabilities such as single-point failures and communication bottlenecks. This paper presents a fully decentralized framework tailored for parameter inference in spatial low-rank models to address these challenges. A key obstacle arises from the spatial ...
• 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...