Journal of the Royal Statistical Society: Series B

最新文章

• 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
• Derandomised knockoffs: leveraging e-values for false discovery rate control AI翻译
  作者：Ren, Zhimei; Barber, Rina Foygel
  作者单位：University of Pennsylvania; University of Chicago
  摘要：Model-X knockoffs is a flexible wrapper method for high-dimensional regression algorithms, which provides guaranteed control of the false discovery rate (FDR). Due to the randomness inherent to the method, different runs of model-X knockoffs on the same dataset often result in different sets of selected variables, which is undesirable in practice. In this article, we introduce a methodology for derandomising model-X knockoffs with provable FDR control. The key insight of our proposed method li...
• Christian Hennig's contribution to the Discussion of 'Safe testing' by Grunwald, de Heide, and Koolen AI翻译
  作者：Hennig, Christian
  作者单位：University of Bologna
• Seconder of the vote of thanks to Crane and Xu and contribution to the Discussion of 'Root and community inference on the latent growth process of a network' AI翻译
  作者：Rubin-Delanchy, Patrick
  作者单位：University of Edinburgh
• Daniela Cialfi's contribution to the Discussion of 'the Discussion Meeting on Probabilistic and statistical aspects of machine learning' AI翻译
  作者：Cialfi, Daniela
  作者单位：Consiglio Nazionale delle Ricerche (CNR); Istituto dei Sistemi Complessi (ISC-CNR)
• Interpretable discriminant analysis for functional data supported on random nonlinear domains with an application to Alzheimer's disease AI翻译
  作者：Lila, Eardi; Zhang, Wenbo; Levendovszky, Swati Rane
  作者单位：University of Washington; University of Washington Seattle; University of California System; University of California Irvine; University of Washington; University of Washington Seattle
  摘要：We introduce a novel framework for the classification of functional data supported on nonlinear, and possibly random, manifold domains. The motivating application is the identification of subjects with Alzheimer's disease from their cortical surface geometry and associated cortical thickness map. The proposed model is based upon a reformulation of the classification problem as a regularized multivariate functional linear regression model. This allows us to adopt a direct approach to the estima...
• David Draper and Erdong Guo's contribution to the discussion of 'Martingale posterior distributions', by Fong, Holmes and Walker AI翻译
  作者：Draper, David; Guo, Erdong
  作者单位：University of California System; University of California Santa Cruz; University of California System; University of California Santa Cruz
• Two-way dynamic factor models for high-dimensional matrix-valued time series AI翻译
  作者：Yuan, Chaofeng; Gao, Zhigen; He, Xuming; Huang, Wei; Guo, Jianhua
  作者单位：Northeast Normal University - China; Northeast Normal University - China; Heilongjiang University; Heilongjiang University; Northeast Normal University - China; Washington University (WUSTL); Beijing Technology & Business University
  摘要：In this article, we introduce a two-way dynamic factor model (2w-DFM) for high-dimensional matrix-valued time series and study some of the basic theoretical properties in terms of identifiability and estimation accuracy. The proposed model aims to capture separable and low-dimensional effects of row and column attributes and their correlations across rows, columns, and time points. Complementary to other dynamic factor models for high-dimensional data, the 2w-DFM inherits the dimension-reducti...
• Semi-parametric estimation of treatment effects in randomised experiments AI翻译
  作者：Athey, Susan; Bickel, Peter J.; Chen, Aiyou; Imbens, Guido W.; Pollmann, Michael
  作者单位：Stanford University; National Bureau of Economic Research; University of California System; University of California Berkeley; Alphabet Inc.; Google Incorporated; Stanford University; Duke University
  摘要：We develop new semi-parametric methods for estimating treatment effects. We focus on settings where the outcome distributions may be thick tailed, where treatment effects may be small, where sample sizes are large, and where assignment is completely random. This setting is of particular interest in recent online experimentation. We propose using parametric models for the treatment effects, leading to semi-parametric models for the outcome distributions. We derive the semi-parametric efficiency...
• David R. Bickel's contribution to the Discussion of 'Safe testing' by Grunwald, De Heide, and Koolen AI翻译
  作者：Bickel, David R.
  作者单位：University of North Carolina; University of North Carolina Greensboro