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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：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：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：Song, Peter X-K; Zhou, Ling
作者单位：University of Michigan System; University of Michigan; Southwestern University of Finance & Economics - China; Southwestern University of Finance & Economics - China
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作者：Chu, J.; Lu, W.; Yang, S.
作者单位：North Carolina State University
摘要：Personalized decision-making, aiming to derive optimal treatment regimes based on individual characteristics, has recently attracted increasing attention in many fields, such as medicine, social services and economics. Current literature mainly focuses on estimating treatment regimes from a single source population. In real-world applications, the distribution of a target population can be different from that of the source population. Therefore, treatment regimes learned by existing methods ma...
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作者：Gutknecht, A. J.; Barnett, L.
作者单位：University of Gottingen; University of Sussex
摘要：The single-regression Granger-Geweke causality estimator has previously been shown to solve known problems associated with the more conventional likelihood ratio estimator; however, its sampling distribution has remained unknown. We show that, under the null hypothesis of vanishing Granger causality, the single-regression estimator converges to a generalized ?(2) distribution, which is well approximated by a & UGamma; distribution. We show that this holds too for Geweke's spectral causality av...
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作者：Ward, S.; Battey, H. S.; Cohen, E. A. K.
作者单位：Imperial College London
摘要：This paper is concerned with nonparametric estimation of the intensity function of a point process on a Riemannian manifold. It provides a first-order asymptotic analysis of the proposed kernel estimator for Poisson processes, supplemented by empirical work to probe the behaviour in finite samples and under other generative regimes. The investigation highlights the scope for finite-sample improvements by allowing the bandwidth to adapt to local curvature.
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作者：Mccormack, A.; Hoff, P. D.
作者单位：Duke University
摘要：The Frechet mean generalizes the concept of a mean to a metric space setting. In this work we consider equivariant estimation of Frechet means for parametric models on metric spaces that are Riemannian manifolds. The geometry and symmetry of such a space are partially encoded by its isometry group of distance-preserving transformations. Estimators that are equivariant under the isometry group take into account the symmetry of the metric space. For some models, there exists an optimal equivaria...
