Journal of the American Statistical Association

最新文章

• Groupwise Dimension Reduction via Envelope Method AI翻译
  作者：Guo, Zifang; Li, Lexin; Lu, Wenbin; Li, Bing
  作者单位：Merck & Company; Merck & Company USA; Stanford University; Li Ka Shing Center; University of California System; University of California Berkeley; North Carolina State University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：The family of sufficient dimension reduction (SDR) methods that produce informative combinations of predictors, or indices, are particularly useful for high-dimensional regression analysis. In many such analyses, it becomes increasingly common that there is available a priori subject knowledge of the predictors; for example, they belong to different groups. While many recent SDR proposals have greatly expanded the scope of the methods' applicability, how to effectively incorporate the prior pr...
• Bayesian Factorizations of Big Sparse Tensors AI翻译
  作者：Zhou, Jing; Bhattacharya, Anirban; Herring, Amy H.; Dunson, David B.
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; Texas A&M University System; Texas A&M University College Station; University of North Carolina; University of North Carolina Chapel Hill; Duke University
  摘要：It has become routine to collect data that are structured as multiway arrays (tensors). There is an enormous literature on low rank and sparse matrix factorizations, but limited consideration of extensions to the tensor case in statistics. The most common low rank tensor factorization relies on parallel factor analysis (PARAFAC), which expresses a rank k tensor as a sum of rank one tensors. In contingency table applications in which the sample size is massively less than the number of cells in...
• False Discovery Rate Regression: An Application to Neural Synchrony Detection in Primary Visual Cortex AI翻译
  作者：Scott, James G.; Kelly, Ryan C.; Smith, Matthew A.; Zhou, Pengcheng; Kass, Robert E.
  作者单位：University of Texas System; University of Texas Austin; Alphabet Inc.; Google Incorporated; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Carnegie Mellon University; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Carnegie Mellon University
  摘要：This article introduces false discovery rate regression, a method for incorporating covariate information into large-scale multiple-testing problems. FDR regression estimates a relationship between test-level covariates and the prior probability that a given observation is a signal. It then uses this estimated relationship to inform the outcome of each test in a way that controls the overall false discovery rate at a prespecified level. This poses many subtle issues at the interface between in...
• An Adaptive Resampling Test for Detecting the Presence of Significant Predictors Comment AI翻译
  作者：Belloni, Alexandre; Chernozhukov, Victor
  作者单位：Duke University; Massachusetts Institute of Technology (MIT)
• Latent Space Models for Dynamic Networks AI翻译
  作者：Sewell, Daniel K.; Chen, Yuguo
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign
  摘要：Dynamic networks are used in a variety of fields to represent the structure and evolution of the relationships between entities. We present a model which embeds longitudinal network data as trajectories in a latent Euclidean space. We propose Markov chain Monte Carlo (MCMC) algorithm to estimate the model parameters and latent positions of the actors in the network. The model yields meaningful visualization of dynamic networks, giving the researcher insight into the evolution and the structure...
• Reinforcement Learning Trees AI翻译
  作者：Zhu, Ruoqing; Zeng, Donglin; Kosorok, Michael R.
  作者单位：University of North Carolina; University of North Carolina Chapel Hill
  摘要：In this article, we introduce a new type of tree-based method, reinforcement learning trees (RLT), which exhibits significantly improved performance over traditional methods such as random forests (Breiman 2001) under high-dimensional settings. The innovations are threefold. First, the new method implements reinforcement learning at each selection of a splitting variable during the tree construction processes. By splitting on the variable that brings the greatest future improvement in later sp...
• Blood Flow Velocity Field Estimation Via Spatial Regression With PDE Penalization AI翻译
  作者：Azzimonti, Laura; Sangalli, Laura M.; Secchi, Piercesare; Domanin, Maurizio; Nobile, Fabio
  作者单位：Polytechnic University of Milan; IRCCS Ca Granda Ospedale Maggiore Policlinico; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
  摘要：We propose an innovative method for the accurate estimation of surfaces and spatial fields when prior knowledge of the phenomenon under study is available. The prior knowledge included in the model derives from physics, physiology, or mechanics of the problem at hand, and is formalized in terms of a partial differential equation governing the phenomenon behavior, as well as conditions that the phenomenon has to satisfy at the boundary of the problem domain. The proposed models exploit advanced...
• Joint Bayesian Modeling of Binomial and Rank Data for Primate Cognition AI翻译
  作者：Barney, Bradley J.; Amici, Federica; Aureli, Filippo; Call, Josep; Johnson, Valen E.
  作者单位：University System of Georgia; Kennesaw State University; Max Planck Society; Universidad Veracruzana; Liverpool John Moores University; University of St Andrews; Max Planck Society
  摘要：In recent years, substantial effort has been devoted to methods for analyzing data containing mixed response types, but such techniques typically do not include rank data among the response types. Some unique challenges exist in analyzing rank data, particularly when ties are prevalent. We present techniques for jointly modeling binomial and rank data using Bayesian latent variable models. We apply these techniques to compare the cognitive abilities of nonhuman primates based on their performa...
• Semiparametric Accelerated Failure Time Modeling for Clustered Failure Times From Stratified Sampling AI翻译
  作者：Chiou, Sy Han; Kang, Sangwook; Yan, Jun
  作者单位：University of Minnesota System; University of Minnesota Duluth; Yonsei University; University of Connecticut; University of Connecticut; University of Connecticut
  摘要：Clustered failure times often arise from studies with stratified sampling designs where it is desired to reduce both cost and sampling error. Semiparametric accelerated failure time (AFT) models have not been used as frequently as Cox relative risk models in such settings due to lack of efficient and reliable computing routines for inferences. The challenge roots in the nonsmoothness of the rank-based estimating functions, and for clustered data, the asymptotic properties of the estimator from...
• Size and Shape Analysis of Error-Prone Shape Data AI翻译
  作者：Du, Jiejun; Dryden, Ian L.; Huang, Xianzheng
  作者单位：University of Nottingham; University of South Carolina System; University of South Carolina Columbia
  摘要：We consider the problem of comparing sizes and shapes of objects when landmark data are prone to measurement error. We show that naive implementation of ordinary Procrustes analysis that ignores measurement error can compromise inference. To account for measurement error, we propose the conditional score method for matching configurations, which guarantees consistent inference under mild model assumptions. The effects of measurement error on inference from naive Procrustes analysis and the per...