Journal of the Royal Statistical Society: Series B

最新文章

• 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...
• Semiparametric localized principal stratification analysis with continuous strata AI翻译
  作者：Zhang, Yichi; Yang, Shu
  作者单位：Indiana University System; Indiana University Bloomington; North Carolina State University
  摘要：Principal stratification is essential for revealing causal mechanisms involving post-treatment intermediate variables, in real-world applications like surrogate marker evaluation. Principal stratification analysis with continuous intermediate variables is increasingly common but challenging due to the infinite principal strata and the nonidentifiability and nonregularity of principal causal effects (PCEs). Inspired by recent research, we resolve these challenges by first using a flexible copul...
• Goodness-of-fit tests for high-dimensional Gaussian graphical models via exchangeable sampling AI翻译
  作者：Lin, Xiaotong; Li, Weihao; Tian, Fangqiao; Huang, Dongming
  作者单位：National University of Singapore; National University of Singapore
  摘要：We introduce a general framework for testing goodness-of-fit for Gaussian graphical models in both the low- and high-dimensional settings. This framework is based on a novel algorithm for generating exchangeable copies by conditioning on sufficient statistics. This framework provides exact finite-sample error control regardless of the dimension and allows flexible choices of test statistics to improve power. We explore several candidate test statistics and conduct extensive simulation studies ...
• Product centred Dirichlet processes for Bayesian multiview clustering AI翻译
  作者：Dombowsky, Alexander; Dunson, David B.
  作者单位：Duke University; Duke University
  摘要：While there is an immense literature on Bayesian methods for clustering, the multiview case has received little attention. This problem focuses on obtaining distinct but statistically dependent clusterings in a common set of entities for different data types. For example, clustering patients into subgroups with subgroup membership varying according to the domain of the patient variables. A challenge is how to model the across-view dependence between the partitions of patients into subgroups. T...
• Adaptive experiments toward learning treatment effect heterogeneity AI翻译
  作者：Wei, Waverly; Ma, Xinwei; Wang, Jingshen
  作者单位：University of Southern California; University of California System; University of California San Diego
  摘要：Understanding treatment effect heterogeneity has become an increasingly popular task in various fields, as it helps design personalized advertisements in e-commerce or targeted treatment in biomedical studies. However, most of the existing work in this research area focused on either analysing observational data based on strong causal assumptions or conducting post hoc analyses of randomized controlled trial data, and there has been limited effort dedicated to the design of randomized experime...
• 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...