2026

最新文章

• Simon et al.'s contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al. AI翻译
  作者：Simon, Horst; Azz, Mohammed El-Amine; El Moukari, Roy; Porcu, Emilio
  作者单位：Khalifa University of Science & Technology
• Yanbo Tang's contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al. AI翻译
  作者：Tang, Yanbo
  作者单位：Imperial College London
• Fukang Zhu and Xiangyu Guo's contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al. AI翻译
  作者：Zhu, Fukang; Guo, Xiangyu
  作者单位：Jilin University; Hebei University of Economics & Business
• Bayesian quantile estimation and regression with martingale posteriors AI翻译
  作者：Fong, Edwin; Yiu, Andrew
  作者单位：University of Hong Kong; University of Southampton
  摘要：Quantile estimation and regression within the Bayesian framework is challenging as the choice of likelihood and prior is not obvious. In this paper, we introduce a novel Bayesian nonparametric method for quantile estimation and regression based on the recently introduced martingale posterior (MP) framework. The core idea of the MP is that posterior sampling is equivalent to predictive imputation, which allows us to break free of the stringent likelihood-prior specification. We demonstrate that...
• Bing Cheng and Howell Tong's contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al. AI翻译
  作者：Cheng, Bing; Tong, Howell
  作者单位：Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; University of London; London School Economics & Political Science
• Sanna Passino and Heard's contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al. AI翻译
  作者：Sanna Passino, Francesco; Heard, Nicholas A.
  作者单位：Imperial College London
• Proposer of the vote of thanks to Bruns-Smith et al. and contribution to the Discussion of 'Augmented balancing weights as linear regression' AI翻译
  作者：Liu, Lin
  作者单位：Shanghai Jiao Tong University
• Inference on strongly identified functionals of weakly identified functions AI翻译
  作者：Bennett, Andrew; Kallus, Nathan; Mao, Xiaojie; Newey, Whitney K.; Syrgkanis, Vasilis; Uehara, Masatoshi
  作者单位：Morgan Stanley; Cornell University; Netflix, Inc.; Tsinghua University; Tsinghua University; Massachusetts Institute of Technology (MIT); Stanford University; Chan Zuckerberg Initiative (CZI)
  摘要：In a variety of applications, including nonparametric instrumental variable (NPIV) analysis, proximal causal inference under unmeasured confounding, and analysis of missing-not-at-random data with shadow variables, we are interested in inference on a continuous linear functional (e.g. average causal effects) of nuisance functions (e.g. NPIV regression) defined by conditional moment restrictions. These nuisance functions are often weakly identified, meaning the moment restrictions are ill-posed...
• The promises of multiple experiments: identifying joint distribution of potential outcomes AI翻译
  作者：Wu, Peng; Mao, Xiaojie
  作者单位：Beijing Technology & Business University; Tsinghua University; Tsinghua University
  摘要：Typical causal effects are defined based on the marginal distribution of potential outcomes. However, many real-world applications require causal estimands involving the joint distribution of potential outcomes to enable nuanced treatment evaluation and selection. In this article, we propose a novel framework for identifying and estimating the joint distribution of potential outcomes using multiple experimental datasets. We introduce the assumption of transportability of state transition proba...
• Authors' reply to discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al. AI翻译
  作者：Whiteley, Nick; Gray, Annie; Rubin-Delanchy, Patrick
  作者单位：University of Bristol; Alan Turing Institute; University of Edinburgh; Heriot Watt University; University of Edinburgh