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作者：Wang, Lijia; Wang, Y. X. Rachel; Li, Jingyi Jessica; Tong, Xin
作者单位：City University of Hong Kong; University of Sydney; University of California System; University of California Los Angeles; University of Southern California
摘要：COVID-19 has a spectrum of disease severity, ranging from asymptomatic to requiring hospitalization. Understanding the mechanisms driving disease severity is crucial for developing effective treatments and reducing mortality rates. One way to gain such understanding is using a multi-class classification framework, in which patients' biological features are used to predict patients' severity classes. In this severity classification problem, it is beneficial to prioritize the identification of m...
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作者：Menacher, Anna; Nichols, Thomas E.; Holmes, Chris; Ganjgahi, Habib
作者单位：University of Oxford; University of Oxford
摘要：Neural demyelination and brain damage accumulated in white matter appear as hyperintense areas on T2-weighted MRI scans in the form of lesions. Modeling binary images at the population level, where each voxel represents the existence of a lesion, plays an important role in understanding aging and inflammatory diseases. We propose a scalable hierarchical Bayesian spatial model, called BLESS, capable of handling binary responses by placing continuous spike-and-slab mixture priors on spatially va...
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作者：Ogburn, Elizabeth L.; Sofrygin, Oleg; Diaz, Ivan; van der Laan, Mark J.
作者单位：Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Kaiser Permanente; Cornell University; Weill Cornell Medicine; University of California System; University of California Berkeley
摘要：We describe semiparametric estimation and inference for causal effects using observational data from a single social network. Our asymptotic results are the first to allow for dependence of each observation on a growing number of other units as sample size increases. In addition, while previous methods have implicitly permitted only one of two possible sources of dependence among social network observations, we allow for both dependence due to transmission of information across network ties an...
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作者：Bilodeau, Blair; Stringer, Alex; Tang, Yanbo
作者单位：University of Toronto; University of Waterloo; Imperial College London
摘要：We provide the first stochastic convergence rates for a family of adaptive quadrature rules used to normalize the posterior distribution in Bayesian models. Our results apply to the uniform relative error in the approximate posterior density, the coverage probabilities of approximate credible sets, and approximate moments and quantiles, therefore guaranteeing fast asymptotic convergence of approximate summary statistics used in practice. The family of quadrature rules includes adaptive Gauss-H...
