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作者：Schultheiss, Christoph; Buhlmann, Peter; Yuan, Ming
作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich; Columbia University
摘要：We introduce a simple diagnostic test for assessing the overall or partial goodness of fit of a linear causal model with errors being independent of the covariates. In particular, we consider situations where hidden confounding is potentially present. We develop a method and discuss its capability to distinguish between covariates that are confounded with the response by latent variables and those that are not. Thus, we provide a test and methodology for partial goodness of fit. The test is ba...
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作者：Cho, Sungho; Jeon, Jeong Min; Kim, Dongwoo; Yu, Kyusang; Park, Byeong U. U.
作者单位：Seoul National University (SNU); KU Leuven; University of Namur; Konkuk University
摘要：In this article we develop semiparametric regression techniques for fitting partially linear additive models. The methods are for a general Hilbert-space-valued response. They use a powerful technique of additive regression in profiling out the additive nonparametric components of the models, which necessarily involves additive regression of the nonadditive effects of covariates. We show that the estimators of the parametric components are root n-consistent and asymptotically Gaussian under we...
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作者：Wang, Shiying; Constable, Todd; Zhang, Heping; Zhao, Yize
作者单位：Yale University; Yale University
摘要：Brain functional connectivity or connectome, a unique measure for brain functional organization, provides a great potential to explain the neurobiological underpinning of behavioral profiles. Existing connectome-based analyses highly concentrate on brain activities under a single cognitive state, and fail to consider heterogeneity when attempting to characterize brain-to-behavior relationships. In this work, we study the complex impact of multi-state functional connectivity on behaviors by ana...
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作者：Zhao, Ruixuan; Zhang, Haoran; Wang, Junhui
作者单位：City University of Hong Kong; Southern University of Science & Technology; Chinese University of Hong Kong
摘要：The chain graph model admits both undirected and directed edges in one graph, where symmetric conditional dependencies are encoded via undirected edges and asymmetric causal relations are encoded via directed edges. Though frequently encountered in practice, the chain graph model has been largely under investigated in the literature, possibly due to the lack of identifiability conditions between undirected and directed edges. In this article, we first establish a set of novel identifiability c...
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作者：Hoshino, Tadao; Yanagi, Takahide
作者单位：Waseda University; Kyoto University
摘要：We consider a causal inference model in which individuals interact in a social network and they may not comply with the assigned treatments. In particular, we suppose that the form of network interference is unknown to researchers. To estimate meaningful causal parameters in this situation, we introduce a new concept of exposure mapping, which summarizes potentially complicated spillover effects into a fixed dimensional statistic of instrumental variables. We investigate identification conditi...
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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...
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作者：Schweinberger, Michael; Fritz, Cornelius
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
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作者：Bertanha, Marinho; Chung, Eunyi
作者单位：University of Notre Dame; University of Illinois System; University of Illinois Urbana-Champaign
摘要：Classical two-sample permutation tests for equality of distributions have exact size in finite samples, but they fail to control size for testing equality of parameters that summarize each distribution. This article proposes permutation tests for equality of parameters that are estimated at root-n or slower rates. Our general framework applies to both parametric and nonparametric models, with two samples or one sample split into two subsamples. Our tests have correct size asymptotically while ...
