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作者：Kafadar, Karen
作者单位：University of Virginia
摘要：What does statistics have to offer science and society, in this age of massive data, machine learning algorithms, and multiple online sources of tools for data analysis? I recall a few situations where statistics made a real difference and reinforced the impact of our discipline on society. Sometimes the difference lay in the insightful analysis and inference enabled by ground-breaking methods in our field like hypothesis testing, likelihood ratios, Bayesian models, jackknife, and bootstrap. B...
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作者：Wager, Stefan
作者单位：Stanford University; Stanford University
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作者：Wang, Qing
作者单位：Wellesley College
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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.
