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作者：Fan, Jianqing; Fan, Yingying; Han, Xiao; Lv, Jinchi
作者单位：Princeton University; University of Southern California; Chinese Academy of Sciences; University of Science & Technology of China, CAS
摘要：Network data are prevalent in many contemporary big data applications in which a common interest is to unveil important latent links between different pairs of nodes. Yet a simple fundamental question of how to precisely quantify the statistical uncertainty associated with the identification of latent links still remains largely unexplored. In this paper, we propose the method of statistical inference on membership profiles in large networks (SIMPLE) in the setting of degree-corrected mixed me...
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作者：Zhao, Qingyuan; Small, Dylan S.; Ertefaie, Ashkan
作者单位：University of Cambridge; University of Pennsylvania; University of Rochester
摘要：Effect modification occurs when the effect of the treatment on an outcome varies according to the level of other covariates and often has important implications in decision-making. When there are tens or hundreds of covariates, it becomes necessary to use the observed data to select a simpler model for effect modification and then make valid statistical inference. We propose a two-stage procedure to solve this problem. First, we use Robinson's transformation to decouple the nuisance parameters...
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作者：Henckel, Leonard; Perkovic, Emilija; Maathuis, Marloes H.
作者单位：University of Copenhagen; University of Washington; University of Washington Seattle; Swiss Federal Institutes of Technology Domain; ETH Zurich
摘要：Covariate adjustment is a commonly used method for total causal effect estimation. In recent years, graphical criteria have been developed to identify all valid adjustment sets, that is, all covariate sets that can be used for this purpose. Different valid adjustment sets typically provide total causal effect estimates of varying accuracies. Restricting ourselves to causal linear models, we introduce a graphical criterion to compare the asymptotic variances provided by certain valid adjustment...
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作者：Lee, Kuang-Yao; Li, Lexin
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; University of California System; University of California Berkeley
摘要：In this article, we introduce a functional structural equation model for estimating directional relations from multivariate functional data. We decouple the estimation into two major steps: directional order determination and selection through sparse functional regression. We first propose a score function at the linear operator level, and show that its minimization can recover the true directional order when the relation between each function and its parental functions is nonlinear. We then d...
