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作者：Feng, Xiangnan; Li, Tengfei; Song, Xinyuan; Zhu, Hongtu
作者单位：Southwest Jiaotong University; University of North Carolina; University of North Carolina Chapel Hill; Chinese University of Hong Kong; University of North Carolina; University of North Carolina Chapel Hill
摘要：Medical imaging has become an increasingly important tool in screening, diagnosis, prognosis, and treatment of various diseases given its information visualization and quantitative assessment. The aim of this article is to develop a Bayesian scalar-on-image regression model to integrate high-dimensional imaging data and clinical data to predict cognitive, behavioral, or emotional outcomes, while allowing for nonignorable missing outcomes. Such a nonignorable nonresponse consideration is motiva...
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作者：Bradley, Jonathan R.; Holan, Scott H.; Wikle, Christopher K.
作者单位：State University System of Florida; Florida State University; University of Missouri System; University of Missouri Columbia
摘要：We introduce a Bayesian approach for analyzing (possibly) high-dimensional dependent data that are distributed according to a member from the natural exponential family of distributions. This problem requires extensive methodological advancements, as jointly modeling high-dimensional dependent data leads to the so-called big n problem. The computational complexity of the big n problem is further exacerbated when allowing for non-Gaussian data models, as is the case here. Thus, we develop new c...
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作者：Ma, Wei; Qin, Yichen; Li, Yang; Hu, Feifang
作者单位：Renmin University of China; University System of Ohio; University of Cincinnati; George Washington University
摘要：Covariate-adaptive randomization (CAR) procedures are frequently used in comparative studies to increase the covariate balance across treatment groups. However, because randomization inevitably uses the covariate information when forming balanced treatment groups, the validity of classical statistical methods after such randomization is often unclear. In this article, we derive the theoretical properties of statistical methods based on general CAR under the linear model framework. More importa...
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作者：Yu, Guan; Li, Quefeng; Shen, Dinggang; Liu, Yufeng
作者单位：State University of New York (SUNY) System; University at Buffalo, SUNY; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; Korea University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
摘要：In modern scientific research, data are often collected from multiple modalities. Since different modalities could provide complementary information, statistical prediction methods using multimodality data could deliver better prediction performance than using single modality data. However, one special challenge for using multimodality data is related to block-missing data. In practice, due to dropouts or the high cost of measures, the observations of a certain modality can be missing complete...
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作者：Zhou, Tingyou; Zhu, Liping; Xu, Chen; Li, Runze
作者单位：Zhejiang University of Finance & Economics; Renmin University of China; University of Ottawa; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：Feature screening plays an important role in the analysis of ultrahigh dimensional data. Due to complicated model structure and high noise level, existing screening methods often suffer from model misspecification and the presence of outliers. To address these issues, we introduce a new metric named cumulative divergence (CD), and develop a CD-based forward screening procedure. This forward screening method is model-free and resistant to the presence of outliers in the response. It also incorp...
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作者：Sung, Chih-Li; Hung, Ying; Rittase, William; Zhu, Cheng; Wu, C. F. Jeff
作者单位：Michigan State University; Rutgers University System; Rutgers University New Brunswick; University System of Georgia; Georgia Institute of Technology; University System of Georgia; Georgia Institute of Technology
摘要：Non-Gaussian observations such as binary responses are common in some computer experiments. Motivated by the analysis of a class of cell adhesion experiments, we introduce a generalized Gaussian process model for binary responses, which shares some common features with standard GP models. In addition, the proposed model incorporates a flexible mean function that can capture different types of time series structures. Asymptotic properties of the estimators are derived, and an optimal predictor ...
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作者：Swanson, S. A.; Hernan, M. A.; Miller, M.; Robins, J. M.; Richardson, T. S.
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作者：Mejia, Amanda F.; Nebel, Mary Beth; Wang, Yikai; Caffo, Brian S.; Guo, Ying
作者单位：Indiana University System; Indiana University Bloomington; Kennedy Krieger Institute; Johns Hopkins University; Emory University; Rollins School Public Health; Johns Hopkins University
摘要：Large brain imaging databases contain a wealth of information on brain organization in the populations they target, and on individual variability. While such databases have been used to study group-level features of populations directly, they are currently underutilized as a resource to inform single-subject analysis. Here, we propose leveraging the information contained in large functional magnetic resonance imaging (fMRI) databases by establishing population priors to employ in an empirical ...
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作者：Lei, Jing
作者单位：Carnegie Mellon University
摘要：Cross-validation is one of the most popular model and tuning parameter selection methods in statistics and machine learning. Despite its wide applicability, traditional cross-validation methods tend to overfit, due to the ignorance of the uncertainty in the testing sample. We develop a novel statistically principled inference tool based on cross-validation that takes into account the uncertainty in the testing sample. This method outputs a set of highly competitive candidate models containing ...
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作者：Grantham, Neal S.; Guan, Yawen; Reich, Brian J.; Borer, Elizabeth T.; Gross, Kevin
作者单位：North Carolina State University; University of Minnesota System; University of Minnesota Twin Cities
摘要：Recent advances in bioinformatics have made high-throughput microbiome data widely available, and new statistical tools are required to maximize the information gained from these data. For example, analysis of high-dimensional microbiome data from designed experiments remains an open area in microbiome research. Contemporary analyses work on metrics that summarize collective properties of the microbiome, but such reductions preclude inference on the fine-scale effects of environmental stimuli ...
