2024

最新文章

• Seconder of the vote of thanks to Grunwald, de Heide, and Koolen and contribution to the Discussion of 'Safe testing' AI翻译
  作者：Martin, Ryan
  作者单位：North Carolina State University
• Semi-parametric tensor factor analysis by iteratively projected singular value decomposition AI翻译
  作者：Chen, Elynn Y.; Xia, Dong; Cai, Chencheng; Fan, Jianqing
  作者单位：New York University; Hong Kong University of Science & Technology; Washington State University; Fudan University; Princeton University
  摘要：This paper introduces a general framework of Semi-parametric TEnsor Factor Analysis (STEFA) that focuses on the methodology and theory of low-rank tensor decomposition with auxiliary covariates. Semi-parametric TEnsor Factor Analysis models extend tensor factor models by incorporating auxiliary covariates in the loading matrices. We propose an algorithm of iteratively projected singular value decomposition (IP-SVD) for the semi-parametric estimation. It iteratively projects tensor data onto th...
• Debiased inference on heterogeneous quantile treatment effects with regression rank scores AI翻译
  作者：Giessing, Alexander; Wang, Jingshen
  作者单位：University of Washington; University of Washington Seattle; University of California System; University of California Berkeley
  摘要：Understanding treatment effect heterogeneity is vital to many scientific fields because the same treatment may affect different individuals differently. Quantile regression provides a natural framework for modelling such heterogeneity. We propose a new method for inference on heterogeneous quantile treatment effects (HQTE) in the presence of high-dimensional covariates. Our estimator combines an l(1)-penalised regression adjustment with a quantile-specific bias correction scheme based on rank ...
• Martin Larsson, Aaditya Ramdas, and Johannes Ruf's contribution to the Discussion of 'Safe testing' by Grünwald, de Heide, and Koolen AI翻译
  作者：Larsson, Martin; Ramdas, Aaditya; Ruf, Johannes
  作者单位：Carnegie Mellon University; Carnegie Mellon University; Carnegie Mellon University; University of London; London School Economics & Political Science
• Andrej Srakars contribution to the Discussion of Root and community inference on the latent growth process of a network by Crane and Xu AI翻译
  作者：Srakar, Andrej
  摘要：Many statistical models for networks overlook the fact that most real-world networks are formed through a growth process. To address this, we introduce the Preferential Attachment Plus Erdos-Renyi model, where we let a random network G be the union of a preferential attachment (PA) tree T and additional Erdos-Renyi (ER) random edges. The PA tree captures the underlying growth process of a network where vertices/edges are added sequentially, while the ER component can be regarded as noise. Give...
• Ivor Cribben and Anastasiou Andreas's contribution to the Discussion of 'the Discussion Meeting on Probabilistic and statistical aspects of machine learning' AI翻译
  作者：Anastasiou, Andreas; Cribben, Ivor
  作者单位：University of Cyprus; University of Alberta
• Shakeel Gavioli-Akilagun's contribution to the Discussion of 'the Discussion Meeting on Probabilistic and statistical aspects of machine learning' AI翻译
  作者：Gavioli-Akilagun, Shakeel
  作者单位：University of London; London School Economics & Political Science
• Thomas S. Richardson and James M. Robins' contribution to the Discussion of 'Parameterizing and simulating from causal models' by Evans and Didelez AI翻译
  作者：Richardson, Thomas S.; Robins, James M.
  作者单位：University of Washington; University of Washington Seattle; Harvard University; Harvard T.H. Chan School of Public Health
• Seconder of the vote of thanks to Evans and Didelez and contribution to the Discussion of 'Parameterizing and simulating from causal models' AI翻译
  作者：Silva, Ricardo
  作者单位：University of London; University College London
• Seconder of the vote of thanks to Fong, Holmes and Walker and contribution to the Discussion of 'Martingale Posterior Distributions' AI翻译
  作者：Fong, Edwin; Holmes, Chris; Lauritzen, Steffen
  作者单位：Alan Turing Institute; University of Oxford; University of Texas System; University of Texas Austin