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作者：Luo, Lan; Wang, Jingshen; Hector, Emily C.
作者单位：University of Iowa; University of California System; University of California Berkeley; North Carolina State University
摘要：Modern longitudinal data, for example from wearable devices, may consist of measurements of biological signals on a fixed set of participants at a diverging number of time-points. Traditional statistical methods are not equipped to handle the computational burden of repeatedly analysing the cumulatively growing dataset each time new data are collected. We propose a new estimation and inference framework for dynamic updating of point estimates and their standard errors along sequentially collec...
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作者：Guo, F. Richard; Perkovic, Emilija; Rotnitzky, Andrea
作者单位：University of Cambridge; University of Washington; University of Washington Seattle; Universidad Torcuato Di Tella
摘要：We study efficient estimation of an interventional mean associated with a point exposure treatment under a causal graphical model represented by a directed acyclic graph without hidden variables. Under such a model, a subset of the variables may be uninformative, in that failure to measure them neither precludes identification of the interventional mean nor changes the semiparametric variance bound for regular estimators of it. We develop a set of graphical criteria that are sound and complete...
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作者：Yu, Haihan; Kaiser, Mark S.; Nordman, Daniel J.
作者单位：Iowa State University
摘要：Bootstrapping spectral mean statistics has been a notoriously difficult problem over the past 25 years. Many frequency domain bootstraps are valid only for certain time series structures, e.g., linear processes, or for special types of statistics, i.e., ratio statistics, because such bootstraps fail to capture the limiting variance of spectral statistics in general settings. We address this issue with a different form of resampling, namely, subsampling. While not considered previously, subsamp...
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作者：Sun, B.; Liu, Z.; Tchetgen, E. J. Tchetgen
作者单位：National University of Singapore; Columbia University; University of Pennsylvania
摘要：The instrumental variable method is widely used in the health and social sciences for identification and estimation of causal effects in the presence of potential unmeasured confounding. To improve efficiency, multiple instruments are routinely used, raising concerns about bias due to possible violation of the instrumental variable assumptions. To address such concerns, we introduce a new class of G-estimators that are guaranteed to remain consistent and asymptotically normal for the causal ef...
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作者：Taylor, Jeremy M. G.; Choi, Kyuseong; Han, Peisong
作者单位：University of Michigan System; University of Michigan; Cornell University
摘要：We consider the situation of estimating the parameters in a generalized linear prediction model, from an internal dataset, where the outcome variable Y is binary and there are two sets of covariates, X and Z. We have information from an external study that provides parameter estimates for a generalized linear model of Y on X. We propose a method that makes limited assumptions about the similarity of the distributions in the two study populations. The method involves orthogonalizing the Z varia...
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作者：Ascolani, F.; Franzolini, B.; Lijoi, A.; Prunster, I
作者单位：Bocconi University
摘要：Modelling of the dependence structure across heterogeneous data is crucial for Bayesian inference, since it directly impacts the borrowing of information. Despite extensive advances over the past two decades, most available methods only allow for nonnegative correlations. We derive a new class of dependent nonparametric priors that can induce correlations of any sign, thus introducing a new and more flexible idea of borrowing of information. This is achieved thanks to a novel concept, which we...
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作者：Ludkin, M.; Sherlock, C.
作者单位：Lancaster University
摘要：This article introduces the hug and hop Markov chain Monte Carlo algorithm for estimating expectations with respect to an intractable distribution. The algorithm alternates between two kernels, referred to as hug and hop. Hug is a nonreversible kernel that repeatedly applies the bounce mechanism from the recently proposed bouncy particle sampler to produce a proposal point that is far from the current position yet on almost the same contour of the target density, leading to a high acceptance p...
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作者：Motta, Giovanni; Wu, Wei Biao; Pourahmadi, Mohsen
作者单位：Texas A&M University System; Texas A&M University College Station; University of Chicago
摘要：Modern statistical methods for multivariate time series rely on the eigendecomposition of matrix-valued functions such as time-varying covariance and spectral density matrices. The curse of indeterminacy or misidentification of smooth eigenvector functions has not received much attention. We resolve this important problem and recover smooth trajectories by examining the distance between the eigenvectors of the same matrix-valued function evaluated at two consecutive points. We change the sign ...
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作者：Wang, Ruoyu; Wang, Qihua; Miao, Wang
作者单位：Chinese Academy of Sciences; Peking University
摘要：Information from multiple data sources is increasingly available. However, some data sources may produce biased estimates due to biased sampling, data corruption or model misspecification. Thus there is a need for robust data combination methods that can be used with biased sources. In this paper, a robust data fusion-extraction method is proposed. Unlike existing methods, the proposed method can be applied in the important case where researchers have no knowledge of which data sources are unb...
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作者：Lou, Zhipeng; Zhang, Xianyang; Wu, Wei Biao
作者单位：Princeton University; Texas A&M University System; Texas A&M University College Station; University of Chicago
摘要：In this paper, we develop a systematic theory for high-dimensional analysis of variance in multivariate linear regression, where the dimension and the number of coefficients can both grow with the sample size. We propose a new U-type statistic to test linear hypotheses and establish a high-dimensional Gaussian approximation result under fairly mild moment assumptions. Our general framework and theory can be used to deal with the classical one-way multivariate analysis of variance, and the nonp...
