Journal of the American Statistical Association

最新文章

• Robustifying Likelihoods by Optimistically Re-weighting Data AI翻译
  作者：Dewaskar, Miheer; Tosh, Christopher; Knoblauch, Jeremias; Dunson, David B.
  作者单位：University of New Mexico; Memorial Sloan Kettering Cancer Center; University of London; University College London; Duke University
  摘要：Likelihood-based inferences have been remarkably successful in wide-spanning application areas. However, even after due diligence in selecting a good model for the data at hand, there is inevitably some amount of model misspecification: outliers, data contamination or inappropriate parametric assumptions such as Gaussianity mean that most models are at best rough approximations of reality. A significant practical concern is that for certain inferences, even small amounts of model misspecificat...
• Hub Detection in Gaussian Graphical Models AI翻译
  作者：Gomez, Jose A. Sanchez; Mo, Weibin; Zhao, Junlong; Liu, Yufeng
  作者单位：University of California System; University of California Riverside; Purdue University System; Purdue University; Beijing Normal University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
  摘要：Graphical models are popular tools for exploring relationships among a set of variables. The Gaussian graphical model (GGM) is an important class of graphical models, where the conditional dependence among variables is represented by nodes and edges in a graph. In many real applications, we are interested in detecting hubs in graphical models, which refer to nodes with a significant higher degree of connectivity compared to non-hub nodes. A typical strategy for hub detection consists of estima...
• A Space Astrophysics Perspective on Poisson-FOCuS AI翻译
  作者：Graziani, Carlo
  作者单位：United States Department of Energy (DOE); Argonne National Laboratory
• Clinical Trial Modernization: Technological, Operational, and Regulatory Advances AI翻译
  作者：Buyse, Marc
• Another Look at High-Dimensional Regression in Principal Components Space and The Blessing of Dimensionality AI翻译
  作者：Song, Yang; Zou, Hui
  作者单位：Alphabet Inc.; Google Incorporated; University of Minnesota System; University of Minnesota Twin Cities
  摘要：Benefits of exploiting sparsity in the space of principal components have been well documented both empirically and theoretically (Lang and Zou; Silin and Fan). In this article, we further reveal another unexpected advantage of exploiting sparsity in the space of principal components when the data are contaminated by measurement errors. Assuming the coefficient vector resides in an lq ball (0 <= q <= 1 ), we show that an l(1) penalized principal components regression has a prediction performan...
• Multi-Scale CUSUM Tests for Time Dependent Spherical Random Fields AI翻译
  作者：Caponera, Alessia; Marinucci, Domenico; Vidotto, Anna
  作者单位：Luiss Guido Carli University; University of Rome Tor Vergata; Sapienza University Rome
  摘要：This article investigates the asymptotic behavior of structural break tests in the harmonic domain for time dependent spherical random fields. In particular, we prove a functional central limit theorem result for the fluctuations over time of the sample spherical harmonic coefficients, under the null of isotropy and stationarity; furthermore, we prove consistency of the corresponding CUSUM test, under a broad range of alternatives, including deterministic trend, abrupt change, and a nontrivial...
• Efficient Human-in-the-Loop Active Learning: A Novel Framework for Data Labeling in AI Systems AI翻译
  作者：Huang, Yiran; Yang, Jian-Feng; Fu, Haoda
  作者单位：Nankai University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
  摘要：Modern AI systems rely heavily on labeled data, yet labeling is often expensive and labor-intensive, especially when requiring special skills such as reading radiology images by physicians. To most efficiently use experts' time for data labeling, one promising approach is human-in-the-loop active learning. However, traditional active learning methods are limited to single-label queries and fail to leverage more flexible query types in many real-world settings. In this work, we propose a novel ...
• The Explicative Market Microstructure Noise AI翻译
  作者：Cui, Wenhao
  作者单位：Beihang University
  摘要：High-frequency financial data are often contaminated by market microstructure effects. In this study, we consider a setting where a portion of the microstructure noise can be explained by observable trading information, referred to as the explicative noise component. To formally analyze this component, we first develop a model-free variable importance measure in the high-frequency setting that quantifies the price impact of subsets of trading variables. Based on the identified significant vari...
• Construction of Asymmetric Nested Orthogonal Arrays AI翻译
  作者：Pang, Shanqi; Lin, Xiao; Ai, Mingyao; Chien, Peter
  作者单位：Henan Normal University; Peking University; Peking University; University of Wisconsin System; University of Wisconsin Madison; Anyang Normal University
  摘要：Nested orthogonal arrays (NOAs), which consist of a pair of orthogonal arrays with one array nested within the other, are extensively used in computer experiments and statistics. They have diverse applications, including data fusion, digital twins, model validation, sequential model evaluation, stochastic programming, chance-constraint problems, nonparametric function estimation, and parameter linking. We propose several general methods for constructing asymmetric NOAs with flexible run sizes,...
• On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization AI翻译
  作者：Xiao, Jiancong; Li, Ziniu; Xie, Xingyu; Getzen, Emily; Fang, Cong; Long, Qi; Su, Weijie J.
  作者单位：University of Pennsylvania; Pennsylvania Medicine; The Chinese University of Hong Kong, Shenzhen; National University of Singapore; Peking University; University of Pennsylvania
  摘要：Accurately aligning large language models (LLMs) with human preferences is crucial for informing fair, economically sound, and statistically efficient decision-making processes. However, we argue that the predominant approach for aligning LLMs with human preferences through a reward model-reinforcement learning from human feedback (RLHF)-suffers from an inherent algorithmic bias due to its Kullback-Leibler-based regularization in optimization. In extreme cases, this bias could lead to a phenom...