Journal of the American Statistical Association

最新文章

• Low-Rank Online Dynamic Assortment with Dual Contextual Information AI翻译
  作者：Lee, Seong Jin; Sun, Will Wei; Liu, Yufeng
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; Purdue University System; Purdue University; University of Michigan System; University of Michigan
  摘要：As e-commerce expands, delivering real-time personalized recommendations from vast catalogs poses a critical challenge for retail platforms. Maximizing revenue requires careful consideration of both individual customer characteristics and available item features to continuously optimize assortments over time. In this article, we consider the dynamic assortment problem with dual contexts-user and item features. In high-dimensional scenarios, the quadratic growth of dimensions complicates comput...
• Boosting AI-Generated Biomedical Images with Confidence through Advanced Statistical Inference AI翻译
  作者：Gu, Zhiling; Yu, Shan; Wang, Guannan; Wang, Lily
  作者单位：Yale University; University of Virginia; George Mason University
  摘要：Generative artificial intelligence (AI) has transformed the biomedical imaging field through image synthesis, addressing challenges of data availability, privacy, and diversity in biomedical research. This article proposes a novel nonparametric method within the functional data framework to discern significant differences between the mean and covariance functions of original and synthetic biomedical imaging data, thereby enhancing the fidelity and utility of synthetic data. Focusing on surface...
• Inference for Low-Rank Models Without Estimating the Rank AI翻译
  作者：Choi, Jungjun; Kwon, Hyukjun; Liao, Yuan
  作者单位：University of Rhode Island; Princeton University; Rutgers University System; Rutgers University New Brunswick
  摘要：This article studies the inference about linear functionals of high-dimensional low-rank matrices. While most existing inference methods would require consistent estimation of the true rank, our procedure is robust to rank misspecification, making it a promising approach in applications where rank estimation can be unreliable. We estimate the low-rank spaces using pre-specified weighting matrices, known as diversified projections. A novel statistical insight is that, unlike the usual statistic...
• Inference on the Proportion of Variance Explained in Principal Component Analysis AI翻译
  作者：Perry, Ronan; Panigrahi, Snigdha; Bien, Jacob; Witten, Daniela
  作者单位：University of Washington; University of Washington Seattle; University of Michigan System; University of Michigan; University of Southern California; University of Washington; University of Washington Seattle
  摘要：Principal component analysis (PCA) is a longstanding approach for dimension reduction. It rests upon the assumption that the underlying signal has low rank, and thus can be well-summarized using a small number of dimensions. The output of PCA is typically represented using a scree plot, which displays the proportion of variance explained (PVE) by each principal component. While the PVE is extensively reported in routine analyses, to the best of our knowledge the notion of inference on the PVE ...
• On a Notion of Graph Centrality Based on L1 Data Depth AI翻译
  作者：Kang, Seungwoo; Oh, Hee-Seok
  作者单位：Seoul National University (SNU); Seoul National University (SNU)
  摘要：A new measure, L-1 centrality, is proposed to assess the centrality of vertices in an undirected and connected graph. The proposed measure can adequately handle graphs with weights assigned to vertices and edges. This study provides tools for graphical and multiscale analysis based on the L-1 centrality. Specifically, the suggested analysis tools include the target plot, L-1 centrality-based neighborhood, and local L-1 centrality. Most importantly, our work is closely associated with the conce...
• Spatial Linear Models for Environmental Data AI翻译
  作者：Sanso, Bruno
  作者单位：University of California System; University of California Santa Cruz
• Distributed Tensor Principal Component Analysis with Data Heterogeneity AI翻译
  作者：Chen, Elynn; Chen, Xi; Jing, Wenbo; Zhang, Yichen
  作者单位：New York University; Purdue University System; Purdue University
  摘要：As tensors become widespread in modern data analysis, Tucker low-rank Principal Component Analysis (PCA) has become essential for dimensionality reduction and structural discovery in tensor datasets. Motivated by the common scenario where large-scale tensors are distributed across diverse geographic locations, this article investigates tensor PCA within a distributed framework where direct data pooling is theoretically suboptimal or practically infeasible. We offer a comprehensive analysis of ...
• Multi-Dimensional Domain Generalization with Low-Rank Structures AI翻译
  作者：Li, Sai; Zhang, Linjun
  作者单位：Renmin University of China; Rutgers University System; Rutgers University New Brunswick
  摘要：In conventional statistical and machine learning methods, it is typically assumed that the test data are identically distributed with the training data. However, this assumption does not always hold, especially in applications where the target population are not well-represented in the training data. This is a notable issue in health-related studies, where specific ethnic populations may be underrepresented, posing a significant challenge for researchers aiming to make statistical inferences a...
• Federated Adaptive Causal Estimation (FACE) of Target Treatment Effects AI翻译
  作者：Han, Larry; Hou, Jue; Cho, Kelly; Duan, Rui; Cai, Tianxi
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Northeastern University; University of Minnesota System; University of Minnesota Twin Cities; US Department of Veterans Affairs; Harvard University; Harvard Medical School
  摘要：Federated learning of causal estimands may greatly improve estimation efficiency by leveraging data from multiple study sites, but robustness to heterogeneity and model misspecifications is vital for ensuring validity. We develop a Federated Adaptive Causal Estimation (FACE) framework to incorporate heterogeneous data from multiple sites to provide treatment effect estimation and inference for a flexibly specified target population of interest. FACE accounts for site-level heterogeneity in the...
• U-Statistic Reduction: Higher-Order Accurate Risk Control and Statistical-Computational Trade-Off AI翻译
  作者：Shao, Meijia; Xia, Dong; Zhang, Yuan
  作者单位：Hong Kong University of Science & Technology; University System of Ohio; Ohio State University
  摘要：U-statistics play central roles in many statistical learning tools but face the haunting issue of scalability. Despite extensive research on accelerating computation by U-statistic reduction, existing results almost exclusively focused on power analysis. Little work addresses risk control accuracy, which requires distinct and much more challenging techniques. In this article, we establish the first statistical inference procedure with provably higher-order accurate risk control for incomplete ...