Journal of the Royal Statistical Society: Series B

最新文章

• Efficient manifold approximation with spherelets AI翻译
  作者：Li, Didong; Mukhopadhyay, Minerva; Dunson, David B.
  作者单位：Princeton University; University of California System; University of California Los Angeles; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Kanpur; Duke University
  摘要：In statistical dimensionality reduction, it is common to rely on the assumption that high dimensional data tend to concentrate near a lower dimensional manifold. There is a rich literature on approximating the unknown manifold, and on exploiting such approximations in clustering, data compression, and prediction. Most of the literature relies on linear or locally linear approximations. In this article, we propose a simple and general alternative, which instead uses spheres, an approach we refe...
• Gaussian Differential Privacy AI翻译
  作者：Chai, Christine P.
  作者单位：Microsoft
• Efficient learning of optimal individualized treatment rules for heteroscedastic or misspecified treatment-free effect models AI翻译
  作者：Mo, Weibin; Liu, Yufeng
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
  摘要：Recent development in data-driven decision science has seen great advances in individualized decision making. Given data with individual covariates, treatment assignments and outcomes, researchers can search for the optimal individualized treatment rule (ITR) that maximizes the expected outcome. Existing methods typically require initial estimation of some nuisance models. The double robustness property that can protect from misspecification of either the treatment-free effect or the propensit...
• Non-reversible parallel tempering: A scalable highly parallel MCMC scheme AI翻译
  作者：Syed, Saifuddin; Bouchard-Cote, Alexandre; Deligiannidis, George; Doucet, Arnaud
  作者单位：University of British Columbia; University of Oxford
  摘要：Parallel tempering (PT) methods are a popular class of Markov chain Monte Carlo schemes used to sample complex high-dimensional probability distributions. They rely on a collection of N interacting auxiliary chains targeting tempered versions of the target distribution to improve the exploration of the state space. We provide here a new perspective on these highly parallel algorithms and their tuning by identifying and formalizing a sharp divide in the behaviour and performance of reversible v...
• ZAP: Z$$ Z $$-value adaptive procedures for false discovery rate control with side information AI翻译
  作者：Leung, Dennis; Sun, Wenguang
  作者单位：University of Melbourne; Zhejiang University
  摘要：Adaptive multiple testing with covariates is an important research direction that has gained major attention in recent years. It has been widely recognised that leveraging side information provided by auxiliary covariates can improve the power of false discovery rate (FDR) procedures. Currently, most such procedures are devised with p-values as their main statistics. However, for two-sided hypotheses, the usual data processing step that transforms the primary statistics, known as z-values, int...
• Gaussian Differential Privacy AI翻译
  作者：Krusche, Peter; Bretz, Frank
  作者单位：Novartis
• Thomas S. Richardson's contribution to the Discussion of 'Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes AI翻译
  作者：Richardson, Thomas S.
  作者单位：University of Washington; University of Washington Seattle
• 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