Journal of the American Statistical Association

最新文章

• Rejoinder AI翻译
  作者：Chen, Rong; Yang, Dan; Zhang, Cun-Hui
  作者单位：Rutgers University System; Rutgers University New Brunswick; University of Hong Kong
• Dynamic Treatment Regimes: Statistical Methods for Precision Medicine AI翻译
  作者：Zhao, Ying-Qi
  作者单位：Fred Hutchinson Cancer Center
• Inference of Breakpoints in High-dimensional Time Series AI翻译
  作者：Chen, Likai; Wang, Weining; Wu, Wei Biao
  作者单位：Washington University (WUSTL); University of Chicago
  摘要：For multiple change-points detection of high-dimensional time series, we provide asymptotic theory concerning the consistency and the asymptotic distribution of the breakpoint statistics and estimated break sizes. The theory backs up a simple two-step procedure for detecting and estimating multiple change-points. The proposed two-step procedure involves the maximum of a MOSUM (moving sum) type statistics in the first step and a CUSUM (cumulative sum) refinement step on an aggregated time serie...
• Optimal Distributed Subsampling for Maximum Quasi-Likelihood Estimators With Massive Data AI翻译
  作者：Yu, Jun; Wang, HaiYing; Ai, Mingyao; Zhang, Huiming
  作者单位：Beijing Institute of Technology; University of Connecticut; Peking University; Peking University; Peking University
  摘要：Nonuniform subsampling methods are effective to reduce computational burden and maintain estimation efficiency for massive data. Existing methods mostly focus on subsampling with replacement due to its high computational efficiency. If the data volume is so large that nonuniform subsampling probabilities cannot be calculated all at once, then subsampling with replacement is infeasible to implement. This article solves this problem using Poisson subsampling. We first derive optimal Poisson subs...
• Joint Structural Break Detection and Parameter Estimation in High-Dimensional Nonstationary VAR Models AI翻译
  作者：Safikhani, Abolfazl; Shojaie, Ali
  作者单位：State University System of Florida; University of Florida; University of Washington; University of Washington Seattle
  摘要：Assuming stationarity is unrealistic in many time series applications. A more realistic alternative is to assume piecewise stationarity, where the model can change at potentially many change points. We propose a three-stage procedure for simultaneous estimation of change points and parameters of high-dimensional piecewise vector autoregressive (VAR) models. In the first step, we reformulate the change point detection problem as a high-dimensional variable selection one, and solve it using a pe...
• Estimating and Accounting for Unobserved Covariates in High-Dimensional Correlated Data AI翻译
  作者：McKennan, Chris; Nicolae, Dan
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; University of Chicago
  摘要：Many high-dimensional and high-throughput biological datasets have complex sample correlation structures, which include longitudinal and multiple tissue data, as well as data with multiple treatment conditions or related individuals. These data, as well as nearly all high-throughput omic data, are influenced by technical and biological factors unknown to the researcher, which, if unaccounted for, can severely obfuscate estimation of and inference on the effects of interest. We therefore develo...
• Mapping the Genetic-Imaging-Clinical Pathway with Applications to Alzheimer's Disease AI翻译
  作者：Yu, Dengdeng; Wang, Linbo; Kong, Dehan; Zhu, Hongtu
  作者单位：University of Texas System; University of Texas Arlington; University of Toronto; University of North Carolina; University of North Carolina Chapel Hill
  摘要：Alzheimer's disease is a progressive form of dementia that results in problems with memory, thinking, and behavior. It often starts with abnormal aggregation and deposition of beta amyloid and tau, followed by neuronal damage such as atrophy of the hippocampi, leading to Alzheimer's disease (AD). The aim of this article is to map the genetic-imaging-clinical pathway for AD in order to delineate the genetically-regulated brain changes that drive disease progression based on the Alzheimer's Dise...
• Low-Rank Covariance Function Estimation for Multidimensional Functional Data AI翻译
  作者：Wang, Jiayi; Wong, Raymond K. W.; Zhang, Xiaoke
  作者单位：Texas A&M University System; Texas A&M University College Station; George Washington University
  摘要：Multidimensional function data arise from many fields nowadays. The covariance function plays an important role in the analysis of such increasingly common data. In this article, we propose a novel nonparametric covariance function estimation approach under the framework of reproducing kernel Hilbert spaces (RKHS) that can handle both sparse and dense functional data. We extend multilinear rank structures for (finite-dimensional) tensors to functions, which allow for flexible modeling of both ...
• Sampling: Design and Analysis, 3rd ed. AI翻译
  作者：Stokes, S. Lynne
  作者单位：Southern Methodist University
• Rejoinder: Confidence Intervals for Nonparametric Empirical Bayes Analysis AI翻译
  作者：Ignatiadis, Nikolaos; Wager, Stefan
  作者单位：Stanford University; Stanford University