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作者：Zheng, Jiayin; Dong, Xinyuan; Newton, Christina C.; Hsu, Li
作者单位：Fred Hutchinson Cancer Center; University of Washington; University of Washington Seattle; American Cancer Society
摘要：Cancer is a heterogeneous disease, and rapid progress in sequencing and -omics technologies has enabled researchers to characterize tumors comprehensively. This has stimulated an intensive interest in studying how risk factors are associated with various tumor heterogeneous features. The Cancer Prevention Study-II (CPS-II) cohort is one of the largest prospective studies, particularly valuable for elucidating associations between cancer and risk factors. In this article, we investigate the ass...
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作者：Hou, Jue; Bradic, Jelena; Xu, Ronghui
作者单位：Harvard University; Harvard T.H. Chan School of Public Health; University of California System; University of California San Diego; University of California System; University of California San Diego; University of California System; University of California San Diego
摘要：Estimating treatment effects for survival outcomes in the high-dimensional setting is critical for many biomedical applications and any application with censored observations. This article establishes an orthogonal score for learning treatment effects, using observational data with a potentially large number of confounders. The estimator allows for root-n, asymptotically valid confidence intervals, despite the bias induced by the regularization. Moreover, we develop a novel hazard difference (...
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作者：Wilms, Ines; Basu, Sumanta; Bien, Jacob; Matteson, David S.
作者单位：Maastricht University; Cornell University; University of Southern California
摘要：The vector autoregressive moving average (VARMA) model is fundamental to the theory of multivariate time series; however, identifiability issues have led practitioners to abandon it in favor of the simpler but more restrictive vector autoregressive (VAR) model. We narrow this gap with a new optimization-based approach to VARMA identification built upon the principle of parsimony. Among all equivalent data-generating models, we use convex optimization to seek the parameterization that is simple...
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作者：Kuchibhotla, Arun K.; Patra, Rohit K.; Sen, Bodhisattva
作者单位：Carnegie Mellon University; State University System of Florida; University of Florida; Columbia University
摘要：We consider estimation and inference in a single-index regression model with an unknown convex link function. We introduce a convex and Lipschitz constrained least-square estimator (CLSE) for both the parametric and the nonparametric components given independent and identically distributed observations. We prove the consistency and find the rates of convergence of the CLSE when the errors are assumed to have only q >= 2 moments and are allowed to depend on the covariates. When q >= 5, we estab...
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作者：Zhou, Kun; Li, Ker-Chau; Zhou, Qing
作者单位：University of California System; University of California Los Angeles; Academia Sinica - Taiwan
摘要：The issue of honesty in constructing confidence sets arises in nonparametric regression. While optimal rate in nonparametric estimation can be achieved and utilized to construct sharp confidence sets, severe degradation of confidence level often happens after estimating the degree of smoothness. Similarly, for high-dimensional regression, oracle inequalities for sparse estimators could be utilized to construct sharp confidence sets. Yet, the degree of sparsity itself is unknown and needs to be...
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作者：Zu, Tianhai; Lian, Heng; Green, Brittany; Yu, Yan
作者单位：University System of Ohio; University of Cincinnati; City University of Hong Kong; University of Louisville
摘要：Despite major advances in research and treatment, identifying important genotype risk factors for high blood pressure remains challenging. Traditional genome-wide association studies (GWAS) focus on one single nucleotide polymorphism (SNP) at a time. We aim to select among over half a million SNPs along with time-varying phenotype variables via simultaneous modeling and variable selection, focusing on the most dangerous blood pressure levels at high quantiles. Taking advantage of rich data fro...
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作者：Zhang, Zhengwu; Wu, Yuexuan; Xiong, Di; Ibrahim, Joseph G.; Srivastava, Anuj; Zhu, Hongtu
作者单位：University of North Carolina; University of North Carolina Chapel Hill; State University System of Florida; Florida State University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill
摘要：Over the past 30 years, magnetic resonance imaging has become a ubiquitous tool for accurately visualizing the change and development of the brain's subcortical structures (e.g., hippocampus). Although subcortical structures act as information hubs of the nervous system, their quantification is still in its infancy due to many challenges in shape extraction, representation, and modeling. Here, we develop a simple and efficient framework of longitudinal elastic shape analysis (LESA) for subcort...
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作者：Cai, Chencheng; Chen, Rong; Xie, Min-ge
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; Rutgers University System; Rutgers University New Brunswick
摘要：Many massive data sets are assembled through collections of information of a large number of individuals in a population. The analysis of such data, especially in the aspect of individualized inferences and solutions, has the potential to create significant value for practical applications. Traditionally, inference for an individual in the dataset is either solely relying on the information of the individual or from summarizing the information about the whole population. However, with the avai...
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作者：Xing, Xin; Zhao, Zhigen; Liu, Jun S.
作者单位：Virginia Polytechnic Institute & State University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; Harvard University
摘要：Simultaneously, finding multiple influential variables and controlling the false discovery rate (FDR) for linear regression models is a fundamental problem. We here propose the Gaussian Mirror (GM) method, which creates for each predictor variable a pair of mirror variables by adding and subtracting a randomly generated Gaussian perturbation, and proceeds with a certain regression method, such as the ordinary least-square or the Lasso (the mirror variables can also be created after selection)....
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作者：Li, Lingzhu; Zhu, Xuehu; Zhu, Lixing
作者单位：Hong Kong Baptist University; University of Alberta; Beijing Normal University; Xi'an Jiaotong University
摘要：In model checking for regressions, nonparametric estimation-based tests usually have tractable limiting null distributions and are sensitive to oscillating alternative models, but suffer from the curse of dimensionality. In contrast, empirical process-based tests can, at the fastest possible rate, detect local alternatives distinct from the null model, yet are less sensitive to oscillating alternatives and rely on Monte Carlo approximation for critical value determination, which is costly in c...
