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作者：Li, Dongdong; Hu, X. Joan; Wang, Rui
作者单位：Harvard Pilgrim Health Care; Harvard University; Harvard Medical School; Simon Fraser University; Harvard University; Harvard T.H. Chan School of Public Health
摘要：This article is concerned with evaluating the association between two event times without specifying the joint distribution parametrically. This is particularly challenging when the observations on the event times are subject to informative censoring due to a terminating event such as death. There are few methods suitable for assessing covariate effects on association in this context. We link the joint distribution of the two event times and the informative censoring time using a nested copula...
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作者：Wang, Bingkai; Susukida, Ryoko; Mojtabai, Ramin; Amin-Esmaeili, Masoumeh; Rosenblum, Michael
作者单位：Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Tehran University of Medical Sciences
摘要：Two commonly used methods for improving precision and power in clinical trials are stratified randomization and covariate adjustment. However, many trials do not fully capitalize on the combined precision gains from these two methods, which can lead to wasted resources in terms of sample size and trial duration. We derive consistency and asymptotic normality of model-robust estimators that combine these two methods, and showthat these estimators can lead to substantial gains in precision and p...
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作者：Tang, Xiwei; Li, Lexin
作者单位：University of Virginia; University of California System; University of California Berkeley
摘要：Point process modeling is gaining increasing attention, as point process type data are emerging in a large variety of scientific applications. In this article, motivated by a neuronal spike trains study, we propose a novel point process regression model, where both the response and the predictor can be a high-dimensional point process. We model the predictor effects through the conditional intensities using a set of basis transferring functions in a convolutional fashion. We organize the corre...
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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：Tang, Weijing; He, Kevin; Xu, Gongjun; Zhu, Ji
作者单位：University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
摘要：This article introduces an Ordinary Differential Equation (ODE) notion for survival analysis. The ODE notion not only provides a unified modeling framework, but more importantly, also enables the development of a widely applicable, scalable, and easy-to-implement procedure for estimation and inference. Specifically, the ODE modeling framework unifies many existing survival models, such as the proportional hazards model, the linear transformation model, the accelerated failure time model, and t...
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作者：Song, Zexi; Tan, Zhiqiang
作者单位：Rutgers University System; Rutgers University New Brunswick
摘要：Various Markov chain Monte Carlo (MCMC) methods are studied to improve upon random walk Metropolis sampling, for simulation from complex distributions. Examples include Metropolis-adjusted Langevin algorithms, Hamiltonian Monte Carlo, and other algorithms related to underdamped Langevin dynamics. We propose a broad class of irreversible sampling algorithms, called Hamiltonian-assisted Metropolis sampling (HAMS), and develop two specific algorithms with appropriate tuning and preconditioning st...
