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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：Accurate uncertainty quantification is crucial for making reliable decisions in various supervised learning scenarios, particularly when dealing with complex, multimodal data such as images and text. Current approaches often face notable limitations, including rigid assumptions and limited generalizability, constraining their effectiveness across diverse supervised learning tasks. To overcome these limitations, we introduce Generative Score Inference (GSI), a flexible inference framework capab...
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作者：Wang, Jianxiang; Le, An M.; Li, Tianxi
作者单位：Rutgers University System; University of California System; University of California Davis; University of Minnesota System; University of Minnesota Twin Cities
摘要：Network-linked data, in which multivariate observations are interconnected by a network, are becoming increasingly prevalent in fields such as sociology and biology. These data often exhibit inherent noise and complex relational structures, complicating conventional modeling and statistical inference. Motivated by empirical challenges in analyzing such datasets, this paper introduces a family of network subspace generalized linear models designed for analyzing noisy, network-linked data. We pr...
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作者：Kaazempur-Mofrad, Ali; Dai, Xiaowu
作者单位：University of California System; University of California Los Angeles
摘要：Kidney exchange programs have substantially increased transplantation rates but also raise critical concerns about fairness in organ allocation. We propose a novel framework leveraging Data Envelopment Analysis (DEA) to evaluate multiple dimensions of fairness-Priority, Access, and Outcome- within a unified model. This approach captures complexities often missed in single-metric analyses. Using data from the United Network for Organ Sharing, we separately quantify fairness across these dimensi...
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作者：Piancastelli, Luiza S. C.; Barreto-Souza, Wagner; Fortin, Norbert J.; Cooper, Keiland W.; Ombao, Hernando
作者单位：University College Dublin; University of California System; University of California Irvine; University of California System; University of California Irvine; King Abdullah University of Science & Technology; King Abdullah University of Science & Technology
摘要：This paper is motivated by neuroscience studies aimed at understanding causal or predictive interactions between nodes in a brain network using multimodal brain activity data. To assess Granger causality, we introduce a flexible framework through a general class of models that accommodate mixed types of data (binary, count, continuous and positive components) formulated in a generalized linear model (GLM) fashion. To conduct statistical inference for causality, we propose a Bayesian mixed time...
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作者：Williamson, Brian D.; Moodie, Erica E. M.; Simon, Gregory E.; Rossom, Rebecca C.; Shortreed, Susan M.
作者单位：Kaiser Permanente; Fred Hutchinson Cancer Center; University of Washington; University of Washington Seattle; McGill University; Kaiser Permanente; Kaiser Permanente; University of Washington; University of Washington Seattle; HealthPartners Institute for Education & Research
摘要：Risk of suicide attempt varies over time. Understanding the importance of risk factors measured at a mental health visit can help clinicians evaluate future risk and provide appropriate care during the visit. In prediction settings where data are collected over time, such as in mental health care, it is often of interest to understand both the importance of variables for predicting the response at each time point and the importance summarized over the time series. Building on recent advances i...
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作者：Metodiev, Martin; Perrot-Dockes, Marie; Ouadah, Sarah; Fosdick, Bailey k.; Robin, Stephane; Latouche, Pierre; Raftery, Adrian E.
作者单位：Universite Clermont Auvergne (UCA); Centre National de la Recherche Scientifique (CNRS); Centre National de la Recherche Scientifique (CNRS); Universite Paris Cite; Centre National de la Recherche Scientifique (CNRS); Sorbonne Universite; Universite Paris Cite; University of Colorado Anschutz; Colorado State University Fort Collins; University of Northern Colorado; Colorado School of Public Health; Institut Universitaire de France; University of Washington; University of Washington Seattle
摘要：We consider the problem of estimating a high-dimensional covariance matrix from a small number of observations when covariates on pairs of variables are available and the variables can have spatial structure. This is motivated by the problem arising in demography of estimating the covariance matrix of the total fertility rate (TFR) of 195 different countries when only 11 observations are available. We construct an estimator for high-dimensional covariance matrices by exploiting information abo...
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作者：Ding, Shengxian; Johns, Emily; Orlichenko, Anton; Fredericks, Carolyn; Zhao, Yize
作者单位：Yale University; Yale University
摘要：Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by amyloid-beta plaques and tau tangles, with significant pathological changes occurring in subcortical brain regions. While previous research has focused primarily on volumetric reductions in areas, such as the hippocampus, thalamus, and caudate, emerging evidence suggests that their fine-grained shape deformations may offer greater sensitivity to early disease pathology. Moreover, understanding how these shape...
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作者：Xiang, Zichang; Sundararajan, Raanju; Ombao, Hernando
作者单位：Southern Methodist University; King Abdullah University of Science & Technology
摘要：Understanding functional connectivity patterns in autism spectrum disorder (ASD) remains an unsettled scientific problem with existing literature pointing to evidence of both over-and underconnectivity. Resting-state fMRI data is a popular modality with rich spatiotemporal information but poses significant modeling challenges. This work presents a new method for identifying neuroimaging biomarkers in ASD using high-dimensional resting-state fMRI data. The proposed model is flexible in that it ...
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作者：Lukemire, Joshua; Wang, Yaotian; Guo, Ying
作者单位：Emory University; Rollins School Public Health
摘要：In recent years, longitudinal, multisite imaging studies have emerged as key tools for investigating brain function. These studies follow a large number of participants for an extended period, offering exciting opportunities to uncover brain functional network changes over time as a function of clinical and demographic covariates. However, these studies also introduce many statistical challenges such as site-effects and accounting for the heterogeneous nature of network differences between sub...
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作者：Mingione, Marco; Di Loro, Pierfrancesco Alaimo; Lagona, Francesco; Maruotti, Antonello
作者单位：Foro Italico University of Rome; Universita LUMSA; Roma Tre University; Khalifa University of Science & Technology; Khalifa University of Science & Technology
摘要：Motivated by the study of pollution trends in the city of Bergen, we introduce a flexible statistical framework for modeling multivariate air pollution data via a nonhomogeneous hidden semi-Markov vector autoregression. The hidden process captures unobserved environmental conditions, while the vector autoregressive structure accounts for temporal autocorrelation and cross-pollutant dependencies. The model further allows time-varying environmental conditions to influence both the average levels...
