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作者：Stoner, Oliver; Economou, Theo; Marques da Silva, Gabriela Drummond
作者单位：University of Exeter; Universidade de Brasilia
摘要：Tuberculosis poses a global health risk and Brazil is among the top 20 countries by absolute mortality. However, this epidemiological burden is masked by under-reporting, which impairs planning for effective intervention. We present a comprehensive investigation and application of a Bayesian hierarchical approach to modeling and correcting under-reporting in tuberculosis counts, a general problem arising in observational count data. The framework is applicable to fully under-reported data, rel...
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作者：Li, Zeda; Krafty, Robert T.
作者单位：City University of New York (CUNY) System; Baruch College (CUNY); Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh
摘要：This article introduces a nonparametric approach to multivariate time-varying power spectrum analysis. The procedure adaptively partitions a time series into an unknown number of approximately stationary segments, where some spectral components may remain unchanged across segments, allowing components to evolve differently over time. Local spectra within segments are fit through Whittle likelihood-based penalized spline models of modified Cholesky components, which provide flexible nonparametr...
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作者：Papadogeorgou, Georgia; Li, Fan
作者单位：Duke University
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作者：Chen, Yang; Meng, Xiao-Li; Wang, Xufei; van Dyk, David A.; Marshall, Herman L.; Kashyap, Vinay L.
作者单位：University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; Harvard University; Imperial College London; Massachusetts Institute of Technology (MIT); Harvard University; Smithsonian Astrophysical Observatory; Smithsonian Institution
摘要：Calibration data are often obtained by observing several well-understood objects simultaneously with multiple instruments, such as satellites for measuring astronomical sources. Analyzing such data and obtaining proper concordance among the instruments is challenging when the physical source models are not well understood, when there are uncertainties in known physical quantities, or when data quality varies in ways that cannot be fully quantified. Furthermore, the number of model parameters i...
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作者：Miller, Jeffrey W.; Dunson, David B.
作者单位：Harvard University; Duke University
摘要：The standard approach to Bayesian inference is based on the assumption that the distribution of the data belongs to the chosen model class. However, even a small violation of this assumption can have a large impact on the outcome of a Bayesian procedure. We introduce a novel approach to Bayesian inference that improves robustness to small departures from the model: rather than conditioning on the event that the observed data are generated by the model, one conditions on the event that the mode...
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作者：Zhao, Qingyuan; Small, Dylan S.; Su, Weijie
作者单位：University of Pennsylvania
摘要：In the evaluation of treatment effects, it is of major policy interest to know if the treatment is beneficial for some and harmful for others, a phenomenon known as qualitative interaction. We formulate this question as a multiple testing problem with many conservative null p-values, in which the classical multiple testing methods may lose power substantially. We propose a simple technique-conditioning-to improve the power. A crucial assumption we need is uniform conservativeness, meaning for ...
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作者：Zhang, Zhengwu; Descoteaux, Maxime; Dunson, David B.
作者单位：University of Rochester; University of Sherbrooke; Duke University
摘要：In studying structural inter-connections in the human brain, it is common to first estimate fiber bundles connecting different regions relying on diffusion MRI. These fiber bundles act as highways for neural activity. Current statistical methods reduce the rich information into an adjacency matrix, with the elements containing a count of fibers or a mean diffusion feature along the fibers. The goal of this article is to avoid discarding the rich geometric information of fibers, developing flex...
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作者：Ren, Zhao; Kang, Yongjian; Fan, Yingying; Lv, Jinchi
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; University of Southern California
摘要：Heterogeneity is often natural in many contemporary applications involving massive data. While posing new challenges to effective learning, it can play a crucial role in powering meaningful scientific discoveries through the integration of information among subpopulations of interest. In this article, we exploit multiple networks with Gaussian graphs to encode the connectivity patterns of a large number of features on the subpopulations. To uncover the underlying sparsity structures across sub...
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作者：Fuglstad, Geir-Arne; Simpson, Daniel; Lindgren, Finn; Rue, Havard
作者单位：Norwegian University of Science & Technology (NTNU); University of Toronto; University of Edinburgh; King Abdullah University of Science & Technology
摘要：Priors are important for achieving proper posteriors with physically meaningful covariance structures for Gaussian random fields (GRFs) since the likelihood typically only provides limited information about the covariance structure under in-fill asymptotics. We extend the recent penalized complexity prior framework and develop a principled joint prior for the range and the marginal variance of one-dimensional, two-dimensional, and three-dimensional Matern GRFs with fixed smoothness. The prior ...
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作者：Huser, Raphael; Wadsworth, Jennifer L.
作者单位：King Abdullah University of Science & Technology; Lancaster University
摘要：Many environmental processes exhibit weakening spatial dependence as events become more extreme. Well-known limiting models, such as max-stable or generalized Pareto processes, cannot capture this, which can lead to a preference for models that exhibit a property known as asymptotic independence. However, weakening dependence does not automatically imply asymptotic independence, and whether the process is truly asymptotically (in)dependent is usually far from clear. The distinction is key as i...
