2026

最新文章

• Gesine Reinert's contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al. AI翻译
  作者：Reinert, Gesine
  作者单位：University of Oxford
• The causal effects of modified treatment policies under network interference AI翻译
  作者：Balkus, Salvador, V; Delaney, Scott W.; Hejazi, Nima S.
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard T.H. Chan School of Public Health
  摘要：Modified treatment policies are a widely applicable class of interventions useful for studying the causal effects of continuous exposures. Approaches to evaluating their causal effects assume no interference, meaning that such effects cannot be learned from data in settings where the exposure of one unit affects the outcomes of others, as is common in spatial or network data. We introduce a new class of intervention-induced modified treatment policies-which we show identify such causal effects...
• Scalability of Metropolis-within-Gibbs schemes for high-dimensional Bayesian models AI翻译
  作者：Ascolani, Filippo; Roberts, Gareth O.; Zanella, Giacomo
  作者单位：Duke University; University of Warwick; Bocconi University; Bocconi University
  摘要：We study general coordinate-wise Markov chain Monte Carlo schemes (such as Metropolis-within-Gibbs samplers), which are commonly used to fit Bayesian non-conjugate hierarchical models. We relate their convergence properties to the ones of the corresponding (potentially not implementable) random scan Gibbs sampler through the notion of conditional conductance. This allows us to study the performances of popular Metropolis-within-Gibbs schemes for non-conjugate hierarchical models, in high-dimen...
• Alberto Bordino and Olga Klopp's contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al. AI翻译
  作者：Bordino, Alberto; Klopp, Olga
  作者单位：University of Warwick; ESSEC Business School
• Milan Stehlík and Martin Schlather's contribution to the Discussion of 'Statistical exploration of the manifold hypothesis' by Whiteley et al. AI翻译
  作者：Stehlik, Milan; Schlather, Martin
  作者单位：Universidad de Valparaiso; University of Mannheim
• Statistical inference for cell type deconvolution AI翻译
  作者：Xie, Dongyue; Gui, Lin; Wang, Jingshu
  作者单位：University of Chicago
  摘要：Integrating heterogeneous datasets across different measurement platforms poses fundamental challenges for statistical inference. An important example is cell type deconvolution, where cell type proportions in bulk RNA-seq data are estimated using reference single-cell data from different sources, leading to platform-specific scaling effects, measurement noise, and biological heterogeneity. Existing methods often treat estimated proportions as observed in downstream analyses, potentially compr...
• Self-organizing state-space models with artificial dynamics AI翻译
  作者：Chen, Yuan; Gerber, Mathieu; Andrieu, Christophe; Douc, Randal
  作者单位：University of Bristol; IMT - Institut Mines-Telecom; Institut Polytechnique de Paris; Telecom SudParis
  摘要：We consider the problem of performing parameter and state inference in a state-space model (SSM) parametrized by a static parameter theta. A popular idea to address this problem consists of incorporating theta in the state of the system and allowing its time evolution, modelled as a Markov chain (theta t)t >= 1. This proxy model defines a so-called self-organizing SSM (SO-SSM) to which one may apply standard particle filters. However, the practical implementation of this idea in a theoreticall...
• Optimal federated learning for functional mean estimation under heterogeneous privacy constraints AI翻译
  作者：Cai, T. Tony; Chakraborty, Abhinav; Vuursteen, Lasse
  作者单位：University of Pennsylvania; Columbia University; Duke University
  摘要：Federated learning (FL) is a distributed machine learning technique designed to preserve data privacy and security, and it has gained significant importance due to its broad range of applications. This paper addresses the problem of optimal functional mean estimation from discretely sampled data in a federated setting. We consider a heterogeneous framework where the number of individuals, measurements per individual, and privacy parameters vary across one or more servers, under both common and...
• Augmented balancing weights as linear regression AI翻译
  作者：Rotnitzky, Andrea; Smucler, Ezequiel; Robins, James
  作者单位：University of Washington; University of Washington Seattle; Harvard University; Harvard T.H. Chan School of Public Health
  摘要：We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning. These popular doubly robust estimators combine outcome modelling with balancing weights-weights that achieve covariate balance directly instead of estimating and inverting the propensity score. When the outcome and weighting models are both linear in some (possibly infinite) basis, we show that the augmented estimator is equivalent to a single linear model with coefficients th...
• Jiawei Shan, Chao Ying, and Jiwei Zhao's contribution to the Discussion of 'Augmented balancing weights as linear regression' by Bruns-Smith et al. AI翻译
  作者：Shan, Jiawei; Ying, Chao; Zhao, Jiwei
  作者单位：University of Wisconsin System; University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison