5期

最新文章

• Joshua Bon and Christian P. Robert's contribution to the Discussion of 'Safe testing' by Grünwald, de Heide, and Koolen AI翻译
  作者：Bon, Joshua; Robert, Christian P.
  作者单位：Universite PSL; Universite Paris-Dauphine; University of Warwick
• Proposer of the vote of thanks to Grünwald, de Heide, and Koolen and contribution to the Discussion of 'Safe testing' AI翻译
  作者：Wang, Ruodu
  作者单位：University of Waterloo
• Ben Swallow's contribution to the Discussion of 'Martingale Posterior Distributions' by Fong, Holmes and Walker AI翻译
  作者：Swallow, Ben
  作者单位：University of St Andrews; University of St Andrews; University of St Andrews
• The DeCAMFounder: nonlinear causal discovery in the presence of hidden variables AI翻译
  作者：Agrawal, Raj; Squires, Chandler; Prasad, Neha; Uhler, Caroline
  作者单位：Massachusetts Institute of Technology (MIT); Harvard University; Massachusetts Institute of Technology (MIT); Broad Institute; Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT)
  摘要：Many real-world decision-making tasks require learning causal relationships between a set of variables. Traditional causal discovery methods, however, require that all variables are observed, which is often not feasible in practical scenarios. Without additional assumptions about the unobserved variables, it is not possible to recover any causal relationships from observational data. Fortunately, in many applied settings, additional structure among the confounders can be expected. In particula...
• Image response regression via deep neural networks AI翻译
  作者：Zhang, Daiwei; Li, Lexin; Sripada, Chandra; Kang, Jian
  作者单位：University of Pennsylvania; University of California System; University of California Berkeley; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
  摘要：Delineating associations between images and covariates is a central aim of imaging studies. To tackle this problem, we propose a novel non-parametric approach in the framework of spatially varying coefficient models, where the spatially varying functions are estimated through deep neural networks. Our method incorporates spatial smoothness, handles subject heterogeneity, and provides straightforward interpretations. It is also highly flexible and accurate, making it ideal for capturing complex...
• Zihao Wen and David L. Dowe's contribution to the Discussion of 'Safe testing' by Grunwald, de Heide, and Koolen AI翻译
  作者：Wen, Zihao; Dowe, David L.
  作者单位：Monash University; South China Agricultural University
• Proximal survival analysis to handle dependent right censoring AI翻译
  作者：Ying, Andrew
  摘要：Many epidemiological and clinical studies aim to analyse a time-to-event endpoint. A common complication is right censoring. In some cases, right censoring occurs when subjects are still surviving after the study terminates or move out of the study area. In such cases, right censoring is typically treated as independent or noninformative. This assumption can be further relaxed to conditional independent censoring by leveraging possibly time-varying covariate information, if available, and assu...
• Statistical inference for multivariate extremes via a geometric approach AI翻译
  作者：Wadsworth, Jennifer L.; Campbell, Ryan
  作者单位：Lancaster University; Lancaster University
  摘要：A geometric representation for multivariate extremes, based on the shapes of scaled sample clouds in light-tailed margins and their so-called limit sets, has recently been shown to connect several existing extremal dependence concepts. However, these results are purely probabilistic, and the geometric approach itself has not been fully exploited for statistical inference. We outline a method for parametric estimation of the limit set shape, which includes a useful non-/semi-parametric estimate...
• Proposer of the vote of thanks to Fong, Holmes and Walker and contribution to the Discussion of 'Martingale Posterior Distributions' AI翻译
  作者：Dawid, Philip
  作者单位：University of Cambridge
  摘要：The prior distribution is the usual starting point for Bayesian uncertainty. In this paper, we present a different perspective that focuses on missing observations as the source of statistical uncertainty, with the parameter of interest being known precisely given the entire population. We argue that the foundation of Bayesian inference is to assign a distribution on missing observations conditional on what has been observed. In the i.i.d. setting with an observed sample of size n, the Bayesia...
• Ramses Mena Chavez's contribution to the Discussion of 'Martingale Posterior Distributions' by Fong, Holmes and Walker AI翻译
  作者：Mena, Ramses H.
  作者单位：Universidad Nacional Autonoma de Mexico; Universidad Nacional Autonoma de Mexico