549期

最新文章

• Functional Data Analysis with R AI翻译
  作者：Kokoszka, Piotr S.
  作者单位：Colorado State University System; Colorado State University Fort Collins
• Generalized Data Thinning Using Sufficient Statistics AI翻译
  作者：Dharamshi, Ameer; Neufeld, Anna; Motwani, Keshav; Gao, Lucy L.; Witten, Daniela; Bien, Jacob
  作者单位：University of Washington; University of Washington Seattle; Fred Hutchinson Cancer Center; University of British Columbia; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Southern California
  摘要：Our goal is to develop a general strategy to decompose a random variable X into multiple independent random variables, without sacrificing any information about unknown parameters. A recent paper showed that for some well-known natural exponential families, X can be thinned into independent random variables X-(1),& mldr;,X-(K) , such that X=& sum;X-K(k=1)(k) . These independent random variables can then be used for various model validation and inference tasks, including in contexts where tradi...
• Testing the Number of Common Factors by Bootstrapped Sample Covariance Matrix in High-Dimensional Factor Models AI翻译
  作者：Yu, Long; Zhao, Peng; Zhou, Wang
  作者单位：Shanghai University of Finance & Economics; Jiangsu Normal University; Jiangsu Normal University; National University of Singapore
  摘要：This article studies the impact of bootstrap procedure on the eigenvalue distributions of the sample covariance matrix under a high-dimensional factor structure. We provide asymptotic distributions for the top eigenvalues of bootstrapped sample covariance matrix under mild conditions. After bootstrap, the spiked eigenvalues which are driven by common factors will converge weakly to Gaussian limits after proper scaling and centralization. However, the largest non-spiked eigenvalue is mainly det...
• Discussion of Data Fission: Splitting a Single Data Point AI翻译
  作者：Neufeld, Anna; Dharamshi, Ameer; Gao, Lucy L.; Witten, Daniela; Bien, Jacob
  作者单位：Williams College; University of Washington; University of Washington Seattle; University of British Columbia; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Southern California
• Saha and Ramdas' Discussion of Poisson-FOCuS by Ward, Dilillo, Eckley & Fearnhead AI翻译
  作者：Saha, Aytijhya; Ramdas, Aaditya
  作者单位：Indian Statistical Institute; Indian Statistical Institute Kolkata; Carnegie Mellon University
• Bayesian Nonparametrics for Causal Inference and Missing Data AI翻译
  作者：Hahn, P. Richard; Daniels, Michael J.; Linero, Antonio; Roy, Jason
  作者单位：Arizona State University; Arizona State University-Tempe
• Quantification of Vaccine Waning as a Challenge Effect AI翻译
  作者：Janvin, Matias; Stensrud, Mats J.
  作者单位：University of Oslo; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
  摘要：Knowing whether vaccine protection wanes over time is important for health policy and drug development. However, quantifying waning effects is difficult. A simple contrast of vaccine efficacy at two different times compares different populations of individuals: those who were uninfected at the first time versus those who remain uninfected until the second time. Thus, the contrast of vaccine efficacy at early and late times can not be interpreted as a causal effect. We propose to quantify vacci...
• Robust Personalized Federated Learning with Sparse Penalization AI翻译
  作者：Liu, Weidong; Mao, Xiaojun; Zhang, Xiaofei; Zhang, Xin
  作者单位：Shanghai Jiao Tong University; Shanghai Jiao Tong University; Zhongnan University of Economics & Law; Iowa State University; Zhongnan University of Economics & Law
  摘要：Federated learning (FL) is an emerging topic due to its advantage in collaborative learning with distributed data. Due to the heterogeneity in the local data-generating mechanism, it is important to consider personalization when developing federated learning methods. In this work, we propose a personalized federated learning (PFL) method to address the robust regression problem. Specifically, we aim to learn the regression weight by solving a Huber loss with the sparse fused penalty. Additiona...
• Assumption-Lean Data Fission with Resampled Data AI翻译
  作者：Fry, Kevin; Panigrahi, Snigdha; Taylor, Jonathan
  作者单位：Stanford University; University of Michigan System; University of Michigan
• Inferring Independent Sets of Gaussian Variables after Thresholding Correlations AI翻译
  作者：Saha, Arkajyoti; Witten, Daniela; Bien, Jacob
  作者单位：University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle; University of Southern California
  摘要：We consider testing whether a set of Gaussian variables, selected from the data, is independent of the remaining variables. This set is selected via a very simple approach: these are the variables for which the correlation with all other variables falls below some threshold. Unlike other settings in selective inference, failure to account for the selection step leads to excessively conservative (as opposed to anti-conservative) results. We propose a new test that conditions on the event that t...