Journal of the Royal Statistical Society: Series B

最新文章

• Federated feature selection with false discovery rate control AI翻译
  作者：Hu, Jie; Tong, Jiayi; Ning, Yang; Tang, Cheng Yong; Moore, Jason H.; Li, Runze; Chen, Yong
  作者单位：University of Pennsylvania; Pennsylvania Medicine; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Cornell University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; Cedars Sinai Medical Center; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：Selecting a set of universally relevant features associated with a given response variable across multiple distributed data sites is an important problem in numerous scientific fields. However, performing this federated feature selection task becomes challenging when individual-level data cannot be shared due to privacy concerns. The problem is further complicated by potential heterogeneity in both feature distributions and model parameters across sites. In this paper, we propose Fed-false dis...
• Additive-Effect Assisted Learning AI翻译
  作者：Zhang, Jiawei; Yang, Yuhong; Ding, Jie
  作者单位：University of Kentucky; University of Minnesota System; University of Minnesota Twin Cities
  摘要：It is quite popular nowadays for researchers and data analysts holding different datasets to seek assistance from each other to enhance their modelling performance. We consider a scenario where different learners hold datasets with potentially distinct variables, and their observations can be aligned by a nonprivate identifier. Their collaboration faces the following difficulties: first, learners may need to keep data values or even variable names undisclosed due to, e.g. commercial interest o...
• Pretraining and the lasso AI翻译
  作者：Craig, Erin; Pilanci, Mert; Le Menestrel, Thomas; Narasimhan, Balasubramanian; Rivas, Manuel A.; Gullaksen, Stein-Erik; Dehghannasiri, Roozbeh; Salzman, Julia; Taylor, Jonathan; Tibshirani, Robert
  作者单位：Stanford University; Stanford University; Stanford University; Stanford University; University of Bergen; Haukeland University Hospital; University of Bergen; Stanford University; Stanford Medicine
  摘要：Pre-training is a powerful paradigm in machine learning to pass information across models. For example, suppose one has a modest-sized dataset of images of cats and dogs and plans to fit a deep neural network to classify them. With pre-training, we start with a neural network trained on a large corpus of images of not just cats and dogs but hundreds of classes. We fix all network weights except the top layer(s) and fine tune on our dataset. This often results in dramatically better performance...
• Identification and multiply robust estimation in causal mediation analysis across principal strata AI翻译
  作者：Cheng, Chao; Li, Fan
  作者单位：Yale University; Yale University
  摘要：We consider assessing causal mediation in the presence of a posttreatment event (examples include noncompliance, a clinical event, or death). We identify natural mediation effects for the entire study population and for each principal stratum characterized by the joint potential values of the posttreatment event. We derive the efficient influence function for each mediation estimand, which motivates a set of multiply robust estimators for inference. The multiply robust estimators are consisten...
• Graphical methods for Order-of-Addition experiments AI翻译
  作者：Rios, Nicholas; Lin, Dennis K. J.
  作者单位：George Mason University; Purdue University System; Purdue University
  摘要：In an Order-of-Addition (OofA) experiment, the order in which m components are added to a system influences a response. Although much research has been done on optimal OofA experiments, existing methodologies typically assume that all m! orders are possible. However, in many practical examples, there are directed constraints on the pairwise order of components, making some of the m! orders infeasible. These constraints can be represented by a directed acyclic graph (DAG). The goal of the OofA ...
• Zhu Shen and Jose R. Zubizarreta's contribution to the Discussion of 'Augmented balancing weights as linear regression' by Bruns-Smith et al. AI翻译
  作者：Shen, Zhu; Zubizarreta, Jose R.
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard Medical School; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University
• Professor Garib Nath Singh's contribution to the Discussion of 'Augmented balancing weights as linear regression' by Bruns-Smith et al. AI翻译
  作者：Singh, Garib Nath
  作者单位：Indian Institute of Technology System (IIT System); Indian Institute of Technology (Indian School of Mines) Dhanbad
• Proposer of the vote of thanks to Whiteley et al. and contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' AI翻译
  作者：Wang, Tengyao
  作者单位：University of London; London School Economics & Political Science
• Bayesian analysis of product feature allocation models AI翻译
  作者：Ghilotti, Lorenzo; Camerlenghi, Federico; Rigon, Tommaso
  作者单位：University of Milano-Bicocca
  摘要：Feature allocation models are an extension of Bayesian nonparametric clustering models, where individuals can share multiple features. We study a broad class of models whose probability distribution has a product form, which includes the popular Indian buffet process. This class plays a prominent role among existing priors, and it shares structural characteristics with Gibbs-type priors in the species sampling framework. We develop a general theory for the entire class, obtaining closed form e...
• Doubly robust conditional independence testing with generative neural networks AI翻译
  作者：Zhang, Yi; Huang, Linjun; Yang, Yun; Shao, Xiaofeng
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University System of Maryland; University of Maryland College Park; Washington University (WUSTL); Washington University (WUSTL)
  摘要：This article addresses the problem of testing the conditional independence of two generic random vectors X and Y given a third random vector Z, which plays an important role in statistical and machine learning applications. We propose a new non-parametric testing procedure that avoids explicitly estimating any conditional distributions but instead requires sampling from the two marginal conditional distributions of X given Z and Y given Z. We further propose using a generative neural network (...