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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：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：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...
