3期

最新文章

• Torben Martinussen's contribution to the Discussion of 'Parameterizing and simulating from causal models' by Evans and Didelez AI翻译
  作者：Martinussen, Torben
  作者单位：University of Copenhagen
• Richard Guo's contribution to the Discussion of 'Parameterizing and simulating from causal models' by Evans and Didelez AI翻译
  作者：Guo, F. Richard
  作者单位：University of Cambridge
• Optimal individualized treatment rule for combination treatments under budget constraints AI翻译
  作者：Xu, Qi; Fu, Haoda; Qu, Annie
  作者单位：University of California System; University of California Irvine; Eli Lilly
  摘要：The individualized treatment rule (ITR), which recommends an optimal treatment based on individual characteristics, has drawn considerable interest from many areas such as precision medicine, personalized education, and personalized marketing. Existing ITR estimation methods mainly adopt 1 of 2 or more treatments. However, a combination of multiple treatments could be more powerful in various areas. In this paper, we propose a novel double encoder model (DEM) to estimate the ITR for combinatio...
• Estimating a directed tree for extremes AI翻译
  作者：Tran, Ngoc Mai; Buck, Johannes; Klueppelberg, Claudia
  作者单位：University of Texas System; University of Texas Austin; Technical University of Munich
  摘要：We propose a new method to estimate a root-directed spanning tree from extreme data. Prominent example is a river network, to be discovered from extreme flow measured at a set of stations. Our new algorithm utilizes qualitative aspects of a max-linear Bayesian network, which has been designed for modelling causality in extremes. The algorithm estimates bivariate scores and returns a root-directed spanning tree. It performs extremely well on benchmark data and on new data. We prove that the new...
• Heather Battey's contribution to the Discussion of 'Parameterizing and simulating from causal models' by Evans and Didelez AI翻译
  作者：Battey, Heather S.
  作者单位：Imperial College London
• Rajendra Bhansali's contribution to the Discussion of 'Parameterizing and simulating from causal models' by Evans and Didelez AI翻译
  作者：Bhansali, Rajendra
  作者单位：University of Liverpool
  摘要：Many statistical problems in causal inference involve a probability distribution other than the one from which data are actually observed; as an additional complication, the object of interest is often a marginal quantity of this other probability distribution. This creates many practical complications for statistical inference, even where the problem is non-parametrically identified. In particular, it is difficult to perform likelihood-based inference, or even to simulate from the model in a ...
• 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...
• 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
• Authors' reply to the Discussion of 'Parameterizing and simulating from causal models' AI翻译
  作者：Evans, Robin J.; Didelez, Vanessa
  作者单位：University of Oxford; Leibniz Association; Leibniz Institute for Prevention Research & Epidemiology (BIPS); University of Bremen; University of Bremen