3期

最新文章

• Yanbo Tang's contribution to the Discussion of 'Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes AI翻译
  作者：Tang, Yanbo
  作者单位：University of Toronto
• Proposer of the vote of thanks and contribution to the Discussion of 'Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes AI翻译
  作者：Daniel, Rhian M.
  作者单位：Cardiff University
• Rachael V. Phillips and Mark J. van der Laan's contribution to the Discussion of 'Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes AI翻译
  作者：Phillips, Rachael, V; van der Laan, Mark J.
  作者单位：University of California System; University of California Berkeley
• High-dimensional changepoint estimation with heterogeneous missingness AI翻译
  作者：Follain, Bertille; Wang, Tengyao; Samworth, Richard J.
  作者单位：University of Cambridge; Inria; Universite PSL; Ecole Normale Superieure (ENS); University of London; London School Economics & Political Science; University of London; University College London
  摘要：We propose a new method for changepoint estimation in partially observed, high-dimensional time series that undergo a simultaneous change in mean in a sparse subset of coordinates. Our first methodological contribution is to introduce a 'MissCUSUM' transformation (a generalisation of the popular cumulative sum statistics), that captures the interaction between the signal strength and the level of missingness in each coordinate. In order to borrow strength across the coordinates, we propose to ...
• Elizabeth L Ogburn, Junhui Cai, Arun K Kuchibhotla, Richard A Berk and Andreas Buja's contribution to the Discussion of 'Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes AI翻译
  作者：Ogburn, Elizabeth L.; Cai, Junhui; Kuchibhotla, Arun K.; Berk, Richard A.; Buja, Andreas
  作者单位：Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; University of Pennsylvania; Carnegie Mellon University; University of Pennsylvania; Simons Foundation; Flatiron Institute
• Anna Choi and Weng Kee Wong's contribution to the Discussion of 'Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes AI翻译
  作者：Choi, Anna; Wong, Weng Kee
  作者单位：Stanford University; University of California System; University of California Los Angeles
• Statistical inference of the value function for reinforcement learning in infinite-horizon settings AI翻译
  作者：Shi, Chengchun; Zhang, Sheng; Lu, Wenbin; Song, Rui
  作者单位：University of London; London School Economics & Political Science; North Carolina State University
  摘要：Reinforcement learning is a general technique that allows an agent to learn an optimal policy and interact with an environment in sequential decision-making problems. The goodness of a policy is measured by its value function starting from some initial state. The focus of this paper was to construct confidence intervals (CIs) for a policy's value in infinite horizon settings where the number of decision points diverges to infinity. We propose to model the action-value state function (Q-functio...
• Andreas Buja, Richard A. Berk, Arun K. Kuchibhotla, Linda Zhao and Ed George's contribution to the Discussion of 'Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes AI翻译
  作者：Buja, Andreas; Berk, Richard A.; Kuchibhotla, Arun K.; Zhao, Linda; George, Ed
  作者单位：University of Pennsylvania; Simons Foundation; Flatiron Institute; University of Pennsylvania; Carnegie Mellon University
• Empirical Bayes PCA in high dimensions AI翻译
  作者：Zhong, Xinyi; Su, Chang; Fan, Zhou
  作者单位：Yale University; Yale University
  摘要：When the dimension of data is comparable to or larger than the number of data samples, principal components analysis (PCA) may exhibit problematic high-dimensional noise. In this work, we propose an empirical Bayes PCA method that reduces this noise by estimating a joint prior distribution for the principal components. EB-PCA is based on the classical Kiefer-Wolfowitz non-parametric maximum likelihood estimator for empirical Bayes estimation, distributional results derived from random matrix t...
• Supervised multivariate learning with simultaneous feature auto-grouping and dimension reduction AI翻译
  作者：She, Yiyuan; Shen, Jiahui; Zhang, Chao
  作者单位：State University System of Florida; Florida State University; Peking University
  摘要：Modern high-dimensional methods often adopt the 'bet on sparsity' principle, while in supervised multivariate learning statisticians may face 'dense' problems with a large number of nonzero coefficients. This paper proposes a novel clustered reduced-rank learning (CRL) framework that imposes two joint matrix regularizations to automatically group the features in constructing predictive factors. CRL is more interpretable than low-rank modelling and relaxes the stringent sparsity assumption in v...