2025

最新文章

• Handbook of Bayesian, Fiducial, and Frequentist Inference AI翻译
  作者：Gu, Mengyang
  作者单位：University of California System; University of California Santa Barbara
• Objective Bayesian Inference AI翻译
  作者：Lee, Jaeyong
  作者单位：Seoul National University (SNU)
• ROC Analysis for Classification and Prediction in Practice AI翻译
  作者：Tec, Mauricio
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health
• Random Effects Model-Based Sufficient Dimension Reduction for Independent Clustered Data AI翻译
  作者：Nghiem, Linh H.; Hui, Francis K. C.
  作者单位：University of Sydney; Australian National University
  摘要：Sufficient dimension reduction (SDR) is a popular class of regression methods which aim to find a small number of linear combinations of covariates that capture all the information of the responses, that is, a central subspace. The majority of current methods for SDR focus on the setting of independent observations, while the few techniques that have been developed for clustered data assume the linear transformation is identical across clusters. In this article, we introduce random effects SDR...
• Kernel Spectral Joint Embeddings for High-Dimensional Noisy Datasets Using Duo-Landmark Integral Operators AI翻译
  作者：Ding, Xiucai; Ma, Rong
  作者单位：University of California System; University of California Davis; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute; Harvard University; Massachusetts Institute of Technology (MIT); Broad Institute
  摘要：Integrative analysis of multiple heterogeneous datasets has arised in many research fields. Existing approaches oftentimes suffer from limited power in capturing nonlinear structures, insufficient account of noisiness and effects of high-dimensionality, lack of adaptivity to signals and sample sizes imbalance, and their results are sometimes difficult to interpret. To address these limitations, we propose a kernel spectral method that achieves joint embeddings of two independently observed hig...
• Dependent Random Partitions by Shrinking Toward an Anchor AI翻译
  作者：Dahl, David B.; Warr, Richard L.; Jensen, Thomas P.
  作者单位：Brigham Young University; Berry Consultants, LLC
  摘要：Although exchangeable processes from Bayesian nonparametrics have been used as a generating mechanism for random partition models, we deviate from this paradigm to explicitly incorporate clustering information in the formulation of our random partition model. Our shrinkage partition distribution takes any partition distribution and shrinks its probability mass toward a specific anchor partition. We show how this provides a framework to model hierarchically-dependent and temporally-dependent ra...
• Optimal Multitask Linear Regression and Contextual Bandits under Sparse Heterogeneity AI翻译
  作者：Huang, Xinmeng; Xu, Kan; Lee, Donghwan; Hassani, Hamed; Bastani, Hamsa; Dobriban, Edgar
  作者单位：University of Pennsylvania; Arizona State University; Arizona State University-Tempe; University of Pennsylvania; University of Pennsylvania; University of Pennsylvania
  摘要：Large and complex datasets are often collected from several, possibly heterogeneous sources. Multitask learning methods improve efficiency by leveraging commonalities across datasets while accounting for possible differences among them. Here, we study multitask linear regression and contextual bandits under sparse heterogeneity, where the source/task-associated parameters are equal to a global parameter plus a sparse task-specific term. We propose a novel two-stage estimator called MOLAR that ...
• Comments on Data Fission: Splitting a Single Data Point AI翻译
  作者：Sarkar, Sanat K.
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University
• Deep Fréchet Regression AI翻译
  作者：Iao, Su I.; Zhou, Yidong; Muller, Hans-Georg
  作者单位：University of California System; University of California Davis
  摘要：Advancements in modern science have led to the increasing availability of non-Euclidean data in metric spaces. This article addresses the challenge of modeling relationships between non-Euclidean responses and multivariate Euclidean predictors. We propose a flexible regression model capable of handling high-dimensional predictors without imposing parametric assumptions. Two primary challenges are addressed: the curse of dimensionality in nonparametric regression and the absence of linear struc...
• Network-Based Neighborhood Regression AI翻译
  作者：Zhen, Yaoming; Du, Jin-Hong
  作者单位：University of Toronto; Carnegie Mellon University; Carnegie Mellon University
  摘要：Given the ubiquity of modularity in biological systems, module-level regulation analysis is vital for understanding biological systems across various levels and their dynamics. Current statistical analysis on biological modules predominantly focuses on either detecting the functional modules in biological networks or sub-group regression on the biological features without using the network data. This article proposes a novel network-based neighborhood regression framework whose regression func...