540期

最新文章

• Moderate-Dimensional Inferences on Quadratic Functionals in Ordinary Least Squares AI翻译
  作者：Guo, Xiao; Cheng, Guang
  作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; Purdue University System; Purdue University
  摘要：Statistical inferences for quadratic functionals of linear regression parameter have found wide applications including signal detection, global testing, inferences of error variance and fraction of variance explained. Classical theory based on ordinary least squares estimator works perfectly in the low-dimensional regime, but fails when the parameter dimension pn grows proportionally to the sample size n. In some cases, its performance is not satisfactory even when n >= 5p(n). The main contrib...
• The Potential of Factor Analysis for Replication, Generalization, and Integration AI翻译
  作者：De Boeck, Paul; DeKay, Michael L.; Xu, Menglin
  作者单位：University System of Ohio; Ohio State University; University System of Ohio; Ohio State University
• Generalized Bayes Quantification Learning under Dataset Shift AI翻译
  作者：Fiksel, Jacob; Datta, Abhirup; Amouzou, Agbessi; Zeger, Scott
  作者单位：Johns Hopkins University; Johns Hopkins University
  摘要：Quantification learning is the task of prevalence estimation for a test population using predictions from a classifier trained on a different population. Quantification methods assume that the sensitivities and specificities of the classifier are either perfect or transportable from the training to the test population. These assumptions are inappropriate in the presence of dataset shift, when the misclassification rates in the training population are not representative of those for the test po...
• A Doubly Enhanced EM Algorithm for Model-Based Tensor Clustering AI翻译
  作者：Mai, Qing; Zhang, Xin; Pan, Yuqing; Deng, Kai
  作者单位：State University System of Florida; Florida State University
  摘要：Modern scientific studies often collect datasets in the form of tensors. These datasets call for innovative statistical analysis methods. In particular, there is a pressing need for tensor clustering methods to understand the heterogeneity in the data. We propose a tensor normal mixture model approach to enable probabilistic interpretation and computational tractability. Our statistical model leverages the tensor covariance structure to reduce the number of parameters for parsimonious modeling...
• Distributed Estimation for Principal Component Analysis: An Enlarged Eigenspace Analysis AI翻译
  作者：Chen, Xi; Lee, Jason D.; Li, He; Yang, Yun
  作者单位：New York University; Princeton University; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：The growing size of modern datasets brings many challenges to the existing statistical estimation approaches, which calls for new distributed methodologies. This article studies distributed estimation for a fundamental statistical machine learning problem, principal component analysis (PCA). Despite the massive literature on top eigenvector estimation, much less is presented for the top-L-dim (L > 1) eigenspace estimation, especially in a distributed manner. We propose a novel multi-round algo...
• Inference on Multi-level Partial Correlations Based on Multi-subject Time Series Data AI翻译
  作者：Qiu, Yumou; Zhou, Xiao-Hua
  作者单位：Iowa State University; Peking University
  摘要：Partial correlations are commonly used to analyze the conditional dependence among variables. In this work, we propose a hierarchical model to study both the subject- and population-level partial correlations based on multi-subject time-series data. Multiple testing procedures adaptive to temporally dependent data with false discovery proportion control are proposed to identify the nonzero partial correlations in both the subject and population levels. A computationally feasible algorithm is d...