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作者：Zhang, Yao; Zhao, Qingyuan
作者单位：Stanford University; University of Cambridge
摘要：The meaning of randomization tests has become obscure in statistics education and practice over the last century. This article makes a fresh attempt at rectifying this core concept of statistics. A new term-quasi-randomization test-is introduced to define significance tests based on theoretical models and distinguish these tests from the randomization tests based on the physical act of randomization. The practical importance of this distinction is illustrated through a real stepped-wedge clust...
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作者：Xu, Tianchen; Chen, Yuan; Zeng, Donglin; Wang, Yuanjia
作者单位：Columbia University; Memorial Sloan Kettering Cancer Center; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
摘要：Digital technologies (e.g., mobile phones) can be used to obtain objective, frequent, and real-world digital phenotypes from individuals. However, modeling these data poses substantial challenges since observational data are subject to confounding and various sources of variabilities. For example, signals on patients' underlying health status and treatment effects are mixed with variation due to the living environment and measurement noises. The digital phenotype data thus shows extensive vari...
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作者：Zhou, Yu; Wang, Lan; Song, Rui; Zhao, Tuoyi
作者单位：University of Miami; North Carolina State University
摘要：In many important applications of precision medicine, the outcome of interest is time to an event (e.g., death, relapse of disease) and the primary goal is to identify the optimal individualized decision rule (IDR) to prolong survival time. Existing work in this area have been mostly focused on estimating the optimal IDR to maximize the restricted mean survival time in the population. We propose a new robust framework for estimating an optimal static or dynamic IDR with time-to-event outcomes ...
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作者：Masak, Tomas; Panaretos, Victor M.
作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
摘要：The problem of covariance estimation for replicated surface-valued processes is examined from the functional data analysis perspective. Considerations of statistical and computational efficiency often compel the use of separability of the covariance, even though the assumption may fail in practice. We consider a setting where the covariance structure may fail to be separable locally-either due to noise contamination or due to the presence of a nonseparable short-range dependent signal componen...
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作者：Tong, Zhaoxue; Cai, Zhanrui; Yang, Songshan; Li, Runze
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Carnegie Mellon University; Renmin University of China
摘要：In this article, we propose a model-free conditional feature screening method with false discovery rate (FDR) control for ultra-high dimensional data. The proposed method is built upon a new measure of conditional independence. Thus, the new method does not require a specific functional form of the regression function and is robust to heavy-tailed responses and predictors. The variables to be conditional on are allowed to be multivariate. The proposed method enjoys sure screening and ranking c...
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作者：Scheike, Thomas H.; Martinussen, Torben; Ozenne, Brice
作者单位：University of Copenhagen
摘要：Direct regression for the cumulative incidence function (CIF) has become increasingly popular since the Fine and Gray model was suggested (Fine and Gray) due to its more direct interpretation on the probability risk scale. We here consider estimation within the Fine and Gray model using the theory of semiparametric efficient estimation. We show that the Fine and Gray estimator is semiparametrically efficient in the case without censoring. In the case of right-censored data, however, we show th...
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作者：Wang, Wenbo; Qiao, Xingye
作者单位：State University of New York (SUNY) System; Binghamton University, SUNY
摘要：This article concerns cautious classification models that are allowed to predict a set of class labels or reject to make a prediction when the uncertainty in the prediction is high. This set-valued classification approach is equivalent to the task of acceptance region learning, which aims to identify subsets of the input space, each of which guarantees to cover observations in a class with at least a predetermined probability. We propose to directly learn the acceptance regions through risk mi...
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作者：Lee, Sze Ming; Sit, Tony; Xu, Gongjun
作者单位：Chinese University of Hong Kong; University of Michigan System; University of Michigan
摘要：Censored quantile regression (CQR) has received growing attention in survival analysis because of its flexibility in modeling heterogeneous effect of covariates. Advances have been made in developing various inferential procedures under different assumptions and settings. Under the conditional independence assumption, many existing CQR methods can be characterized either by stochastic integral-based estimating equations (see, e.g., Peng and Huang) or by locally weighted approaches to adjust fo...
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作者：Gao, Zhaoxing; Tsay, Ruey S.
作者单位：Zhejiang University; University of Chicago
摘要：This article proposes a hierarchical approximate-factor approach to analyzing high-dimensional, large-scale heterogeneous time series data using distributed computing. The new method employs a multiple-fold dimension reduction procedure using Principal Component Analysis (PCA) and shows great promises for modeling large-scale data that cannot be stored nor analyzed by a single machine. Each computer at the basic level performs a PCA to extract common factors among the time series assigned to i...
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作者：Park, Jaewoo
作者单位：Yonsei University
