528期

最新文章

• Choose to Lead AI翻译
  作者：LaVange, Lisa Morrissey
  摘要：Color versions of one or more of the figures in the article can be found online at . Abstract-Each year, the Journal of the American Statistical Association publishes the presidential address from the Joint Statistical Meetings. Here we present the 2018 address verbatim save for the addition of references and a few minor editorial corrections.
• The Blessings of Multiple Causes: Rejoinder AI翻译
  作者：Wang, Yixin; Blei, David M.
  作者单位：Columbia University; Columbia University
• Comment on: The Blessings of Multiple Causes by Yixin Wang and David M. Blei AI翻译
  作者：Athey, Susan; Imbens, Guido W.; Pollmann, Michael
  作者单位：Stanford University; National Bureau of Economic Research; Stanford University
• Time Series Experiments and Causal Estimands: Exact Randomization Tests and Trading AI翻译
  作者：Bojinov, Iavor; Shephard, Neil
  作者单位：Harvard University; Harvard University
  摘要：We define causal estimands for experiments on single time series, extending the potential outcome framework to dealing with temporal data. Our approach allows the estimation of a broad class of these estimands and exact randomization-based p-values for testing causal effects, without imposing stringent assumptions. We further derive a general central limit theorem that can be used to conduct conservative tests and build confidence intervals for causal effects. Finally, we provide three methods...
• A Hierarchical Framework for Correcting Under-Reporting in Count Data AI翻译
  作者：Stoner, Oliver; Economou, Theo; Marques da Silva, Gabriela Drummond
  作者单位：University of Exeter; Universidade de Brasilia
  摘要：Tuberculosis poses a global health risk and Brazil is among the top 20 countries by absolute mortality. However, this epidemiological burden is masked by under-reporting, which impairs planning for effective intervention. We present a comprehensive investigation and application of a Bayesian hierarchical approach to modeling and correcting under-reporting in tuberculosis counts, a general problem arising in observational count data. The framework is applicable to fully under-reported data, rel...
• Nonparametric Bayes Models of Fiber Curves Connecting Brain Regions AI翻译
  作者：Zhang, Zhengwu; Descoteaux, Maxime; Dunson, David B.
  作者单位：University of Rochester; University of Sherbrooke; Duke University
  摘要：In studying structural inter-connections in the human brain, it is common to first estimate fiber bundles connecting different regions relying on diffusion MRI. These fiber bundles act as highways for neural activity. Current statistical methods reduce the rich information into an adjacency matrix, with the elements containing a count of fibers or a mean diffusion feature along the fibers. The goal of this article is to avoid discarding the rich geometric information of fibers, developing flex...
• Tuning-Free Heterogeneous Inference in Massive Networks AI翻译
  作者：Ren, Zhao; Kang, Yongjian; Fan, Yingying; Lv, Jinchi
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; University of Southern California
  摘要：Heterogeneity is often natural in many contemporary applications involving massive data. While posing new challenges to effective learning, it can play a crucial role in powering meaningful scientific discoveries through the integration of information among subpopulations of interest. In this article, we exploit multiple networks with Gaussian graphs to encode the connectivity patterns of a large number of features on the subpopulations. To uncover the underlying sparsity structures across sub...
• Sparse Sliced Inverse Regression via Lasso AI翻译
  作者：Lin, Qian; Zhao, Zhigen; Liu, Jun S.
  作者单位：Tsinghua University; Tsinghua University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; Harvard University
  摘要：For multiple index models, it has recently been shown that the sliced inverse regression (SIR) is consistent for estimating the sufficient dimension reduction (SDR) space if and only if , where p is the dimension and n is the sample size. Thus, when p is of the same or a higher order of n, additional assumptions such as sparsity must be imposed in order to ensure consistency for SIR. By constructing artificial response variables made up from top eigenvectors of the estimated conditional covari...
• Dynamic Tensor Clustering AI翻译
  作者：Sun, Will Wei; Li, Lexin
  作者单位：University of Miami; University of California System; University of California Berkeley
  摘要：Dynamic tensor data are becoming prevalent in numerous applications. Existing tensor clustering methods either fail to account for the dynamic nature of the data, or are inapplicable to a general-order tensor. There is also a gap between statistical guarantee and computational efficiency for existing tensor clustering solutions. In this article, we propose a new dynamic tensor clustering method that works for a general-order dynamic tensor, and enjoys both strong statistical guarantee and high...
• Covariate Information Matrix for Sufficient Dimension Reduction AI翻译
  作者：Yao, Weixin; Nandy, Debmalya; Lindsay, Bruce G.; Chiaromonte, Francesca
  作者单位：University of California System; University of California Riverside; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Scuola Superiore Sant'Anna; Scuola Superiore Sant'Anna
  摘要：Building upon recent research on the applications of the density information matrix, we develop a tool for sufficient dimension reduction (SDR) in regression problems called covariate information matrix (CIM). CIM exhaustively identifies the central subspace (CS) and provides a rank ordering of the reduced covariates in terms of their regression information. Compared to other popular SDR methods, CIM does not require distributional assumptions on the covariates, or estimation of the mean regre...