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作者：Ni, Yang
作者单位：University of Texas System; University of Texas Austin
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作者：Tian, Xinyu; Shen, Xiaotong
作者单位：University of Minnesota System; University of Minnesota Twin Cities
摘要：Reliable machine learning and statistical analysis rely on diverse, well-distributed training data. However, real-world datasets are often limited in size and exhibit underrepresentation across key subpopulations, leading to biased predictions and reduced performance, particularly in supervised tasks such as classification. To address these challenges, we propose Conditional Data Synthesis Augmentation (CoDSA), a novel framework that leverages generative models, such as diffusion models, to sy...
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作者：Shi, Jiaxin; Zhu, Xuening; Zhou, Jing; Yu, Baichen; Wang, Hansheng
作者单位：Peking University; Fudan University; Fudan University; Fudan University; Renmin University of China
摘要：We study one particular type of multivariate spatial autoregression (MSAR) model with diverging dimensions in both responses and covariates. This makes the usual MSAR models no longer applicable due to the high computational cost. To address this issue, we propose a factor-augmented spatial autoregression (FSAR) model. FSAR is a special case of MSAR but with a novel factor structure imposed on the high-dimensional random error vector. The latent factors of FSAR are assumed to be of a fixed dim...
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作者：Wang, Shuqi; Thall, Peter F.; Yuan, Ying; Liu, Suyu
作者单位：University of Texas System; UTMD Anderson Cancer Center
摘要：For first-in-human dose-finding trials, to protect patient safety, regulatory agencies may enforce strict within-cohort staggering rules that require delaying treatment of each patient in the first cohort at an untried dose until dose-limiting toxicities (DLTs) of all previously treated patients have been evaluated. Consequently, many new patients may face therapy delays, which reduces their probability of achieving a response due to disease progression, or be treated off-protocol, which may s...
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作者：Cai, Junhui; Yang, Dan; Chen, Ran; Shen, Haipeng; Zhao, Linda; Zhu, Wu
作者单位：University of Notre Dame; University of Hong Kong; Washington University (WUSTL); University of Pennsylvania; Tsinghua University
摘要：The centrality in a network is often used to measure nodes' importance and model network effects on a certain outcome. Empirical studies widely adopt a two-stage procedure, which first estimates the centrality from the observed noisy network and then infers the network effect from the estimated centrality, even though it lacks theoretical understanding. We propose a unified modeling framework to study the properties of centrality estimation and inference and the subsequent network regression a...
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作者：Xia, Xintao; Zhang, Linjun; Cai, Zhanrui
作者单位：Iowa State University; Rutgers University System; Rutgers University New Brunswick; University of Hong Kong
摘要：Privacy preservation has become a critical concern in high-dimensional data analysis due to the growing prevalence of data-driven applications. Since its proposal, sliced inverse regression has emerged as a widely used statistical technique to reduce the dimensionality of covariates while maintaining sufficient statistical information. In this used, we propose optimally differentially private algorithms specifically designed to address privacy concerns in the context of sufficient dimension re...
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作者：Gai, Xin; Jiang, Shiyi; Zhang, Anru R.
作者单位：Vanderbilt University; Duke University; Duke University; Duke University
摘要：Electronic Health Records (EHRs) contain extensive patient information that can inform downstream clinical decisions, such as mortality prediction, disease phenotyping, and disease onset prediction. A key challenge in EHR data analysis is the temporal gap between when a condition is first recorded and its actual onset time. Such timeline misalignment can lead to artificially distinct biomarker trends among patients with similar disease progression, undermining the reliability of downstream ana...
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作者：Chen, Elynn; Fan, Jianqing; Zhu, Xiaonan
作者单位：New York University; Princeton University
摘要：We introduce Factor-Augmented Matrix Regression (FAMAR) to address the growing applications of matrix-variate data and their associated challenges, particularly with high-dimensionality and covariate correlations. FAMAR encompasses two key algorithms. The first is a novel non-iterative approach that efficiently estimates the factors and loadings of the matrix factor model, using techniques of pre-training, diverse projection, and block-wise averaging. The second algorithm offers an accelerated...
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作者：Yang, Yuepeng; Ma, Cong
作者单位：Yale University; University of Chicago
摘要：The Rasch model, a classical model in the item response theory, is widely used in psychometrics to model the relationship between individuals' latent traits and their binary responses to assessments or questionnaires. In this article, we introduce a new likelihood-based estimator-random pairing maximum likelihood estimator (RP-MLE ) and its bootstrapped variant multiple random pairing MLE (MRP-MLE ) which faithfully estimate the item parameters in the Rasch model. The new estimators have sever...
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作者：Liu, Wei; Lin, Huazhen; Zheng, Shurong; Liu, Jin
作者单位：Sichuan University; Southwestern University of Finance & Economics - China; Southwestern University of Finance & Economics - China; Northeast Normal University - China; The Chinese University of Hong Kong, Shenzhen
