The Annals of Applied Statistics

最新文章

• NETWORK-LEVEL TRAFFIC FLOW PREDICTION: FUNCTIONAL TIME SERIES VS. FUNCTIONAL NEURAL NETWORK APPROACH AI翻译
  作者：Ma, Tao; Yao, Fang; Zhou, Zhou
  作者单位：Texas State University System; Texas State University San Marcos; Peking University; University of Toronto
  摘要：Traffic state prediction is an essential component and an underlying backbone of intelligent transportation systems, especially in the context of smart city framework. Its significance is mainly twofold in modern transportation systems: supporting advanced traffic operations and management for highways and urban road networks to mitigate traffic congestion and enabling individual drivers with connected vehicles in the traffic system to dynamically optimize their routes to improve travel time. ...
• PREDICTING COVID-19 HOSPITALISATION USING A MIXTURE OF BAYESIAN PREDICTIVE SYNTHESES AI翻译
  作者：Kobayashi, Genya; Sugasawa, Shonosuke; Kawakubo, Yuki; Han, Dongu; Choi, Taeryon
  作者单位：Meiji University; Keio University; Chiba University; Korea University
  摘要：This paper proposes a novel methodology called the mixture of Bayesian predictive syntheses (MBPS) for multiple time series count data for the challenging task of predicting the numbers of COVID-19 inpatients and isolated cases in Japan and Korea at the subnational level. MBPS combines a set of predictive models and partitions the multiple time series into clusters based on their contribution to predicting the outcome. In this way MBPS leverages the shared information within each cluster and i...
• IMPLICIT GENERATIVE PRIOR FOR BAYESIAN NEURAL NETWORKS AI翻译
  作者：Liu, Yijia; Wang, Xiao
  作者单位：Purdue University System; Purdue University
  摘要：Predictive uncertainty quantification is crucial for reliable decisionmaking in various applied domains. Bayesian neural networks offer a powerful framework for this task. However, defining meaningful priors and ensuring computational efficiency remain significant challenges, especially for complex real-world applications. This paper addresses these challenges by NA-EB leverages a class of implicit generative priors derived from lowdimensional distributions. This allows for efficient handling ...
• EARLY EFFECTS OF 2014 US MEDICAID EXPANSIONS ON MORTALITY: DESIGN-BASED INFERENCE FOR IMPACTS ON SMALL SUBGROUPS DESPITE SMALL-CELL SUPPRESSION AI翻译
  作者：Mann, Charlotte Z.; Hansen, Ben B.; Gaydosh, Lauren
  作者单位：University of Michigan System; University of Michigan; University of Texas System; University of Texas Austin
  摘要：Since 2014, states in the U.S. can choose whether to adopt Medicaid expansion as part of the Affordable Care Act (ACA), relaxing eligibility requirements. This heterogeneity in policy adoption between states raises the question-would there be a difference in health outcomes for states that have not expanded insurance access if they did expand Medicaid eligibility? In this study we estimate the effect of ACA Medicaid expansion on county-level allcause mortality in the U.S. in 2014 overall and f...
• INDIVIDUAL DYNAMIC PREDICTION FOR CURE AND SURVIVAL BASED ON LONGITUDINAL BIOMARKERS AI翻译
  作者：Xie, Can; Huang, Xuelin; Li, Ruosha; Tsodikov, Alexander; Bhalla, Kapil
  作者单位：University of Texas System; UTMD Anderson Cancer Center; University of Texas System; University of Texas Health Science Center Houston; University of Michigan System; University of Michigan; University of Texas System; UTMD Anderson Cancer Center
  摘要：To optimize personalized treatment strategies and extend patients' survival times, it is critical to accurately predict patients' prognoses at all stages, from disease diagnosis to follow-up visits. The longitudinal biomarker measurements during visits are essential for this prediction purpose. Patients' ultimate concerns are cure and survival. However, in many situations there is no clear biomarker indicator for cure. We propose a comprehensive joint model of longitudinal and survival data an...
• JOINT MIXED MEMBERSHIP MODELING OF MULTIVARIATE LONGITUDINAL AND SURVIVAL DATA FOR LEARNING THE INDIVIDUALIZED DISEASE PROGRESSION AI翻译
  作者：He, Yuyang; Song, Xinyuan; Kang, Kai
  作者单位：Chinese University of Hong Kong; Sun Yat Sen University
  摘要：Patients with Alzheimer's disease (AD) often exhibit substantial heterogeneity in disease progression due to multiple genetic causes for such a complex disease. Investigating diverse subtypes of neurodegeneration and individualized disease progression is essential for early diagnosis and precision medicine. In this article we present a novel joint mixed membership model for multivariate longitudinal AD-related biomarkers and time of AD diagnosis. Unlike conventional finite mixture models that ...
• LATENT CONJUNCTIVE BAYESIAN NETWORK: UNIFY ATTRIBUTE HIERARCHY AND BAYESIAN NETWORK FOR COGNITIVE DIAGNOSIS AI翻译
  作者：Lee, Seunghyun; Gu, Yuqi
  作者单位：Columbia University
  摘要：Cognitive diagnostic assessment aims to measure specific knowledge structures in students. To model data arising from such assessments, cognitive diagnostic models with discrete latent variables have gained popularity in educational and behavioral sciences. In a learning context, the latent variables often denote sequentially acquired skill attributes, which is often modeled by the so-called attribute hierarchy method. One drawback of the traditional attribute hierarchy method is that its para...
• A NONPARAMETRIC MIXED-EFFECTS MIXTURE MODEL FOR PATTERNS OF CLINICAL MEASUREMENTS ASSOCIATED WITH COVID-19 AI翻译
  作者：Ma, Xiaoran; Guo, Wensheg; Gu, Mengyang; Usvyat, Len; Kotanko, Peter; Wang, Yuedong
  作者单位：University of California System; University of California Santa Barbara; University of Pennsylvania; Renal Research Institute
  摘要：Some patients with COVID-19 show changes in signs and symptoms, such as temperature and oxygen saturation days before being positively tested for SARS-CoV-2, while others remain asymptomatic. It is important to identify these subgroups and to understand what biological and clinical predictors are related to these subgroups. This information will provide insights into how the immune system may respond differently to infection and can further be used to identify infected individuals. We propose ...
• A NOVEL BAYESIAN MODEL FOR ASSESSING INTRATUMOR HETEROGENEITY OF TUMOR INFILTRATING LEUKOCYTES WITH MULTIREGION GENE EXPRESSION SEQUENCING AI翻译
  作者：Yang, Peng; Hubert, Shawna M.; Futreal, P. Andrew; Song, Xingzhi; Zhang, Jianhua; Lee, J. Jack; Wistuba, Ignacio; Yuan, Ying; Zhang, Jianjun; Li, Ziyi
  作者单位：Rice University; University of Texas System; UTMD Anderson Cancer Center; University of Texas System; UTMD Anderson Cancer Center; University of Texas System; UTMD Anderson Cancer Center; University of Texas System; UTMD Anderson Cancer Center
  摘要：Intratumor heterogeneity (ITH) of tumor-infiltrated leukocytes (TILs) is an important phenomenon of cancer biology with potentially profound clinical impacts. Multiregion gene expression sequencing data provide a promising opportunity that allows for explorations of TILs and their intratumor heterogeneity for each subject. Although several existing methods are available to infer the proportions of TILs, considerable methodological gaps exist for evaluating intratumor heterogeneity of TILs with...
• RISK-AWARE RESTRICTED OUTCOME LEARNING FOR INDIVIDUALIZED TREATMENT REGIMES OF SCHIZOPHRENIA AI翻译
  作者：Zhu, Shuying; Shen, Weining; Fu, Haoda; Qu, Annie
  作者单位：University of California System; University of California Irvine; Eli Lilly
  摘要：Schizophrenia is a severe mental disorder that distorts patients' perception of reality, and its treatment with antipsychotics can lead to significant side effects. Despite the heterogeneity in patient responses to treatments, most existing studies on individualized treatment regimes only focus on optimizing treatment efficacy, disregarding potential negative effects. To fill this gap, we propose a restricted outcome weighted learning method that optimizes efficacy outcomes while adhering to i...