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作者：Yan, Ting; Li, Yuanzhang; Xu, Jinfeng; Yang, Yaning; Zhu, Ji
作者单位：Central China Normal University; George Washington University; City University of Hong Kong; Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of Michigan System; University of Michigan
摘要：We explore the Wilks phenomena in two random graph models: the beta-model and the Bradley-Terry model. For two increasing dimensional null hypotheses, including a specified null H-0:beta(i)=beta(0)(i) for i=1,...,r and a homogenous null H-0:beta(1)=...=beta(r), we reveal high dimensional Wilks' phenomena that the normalized log-likelihood ratio statistic, [2{l(beta)-l(beta(0))}-r]/(2r)(1/2), converges in distribution to the standard normal distribution as r goes to infinity. Here, l(beta) is t...
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作者：Jiang, Yiran; Liu, Chuanhai
作者单位：Purdue University System; Purdue University
摘要：From a model-building perspective, we propose a paradigm shift for fitting over-parameterized models. Philosophically, the mindset is to fit models to future observations rather than to the observed sample. Technically, given an imputation method to generate future observations, we fit over-parameterized models to these future observations by optimizing an approximation of the desired expected loss function based on its sample counterpart and an adaptive duality function. The required imputati...
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作者：Wang, Changhu; Ge, Xinzhou; Song, Dongyuan; Li, Jingyi Jessica
作者单位：University of California System; University of California Los Angeles; Oregon State University; University of California System; University of California Los Angeles
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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...
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作者：Sanso, Bruno
作者单位：University of California System; University of California Santa Cruz
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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 ...
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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...
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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...
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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 ...
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作者：Zhang, Shuangjie; Shen, Yuning; Chen, Irene A.; Lee, Juhee
作者单位：University of California System; University of California Santa Cruz; University of California System; University of California Los Angeles
摘要：Group factor models have been developed to infer relationships between multiple co-occurring multivariate continuous responses. Motivated by complex count data from multi-domain microbiome studies using next-generation sequencing, we develop a sparse Bayesian group factor model (Sp-BGFM) for multiple count table data that captures the interaction between microorganisms in different domains. Sp-BGFM uses a rounded kernel mixture model using a Dirichlet process (DP) prior with log-normal mixture...
