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作者：Deng, Wanlu; Geng, Zhi; Li, Hongzhe
作者单位：Peking University; University of Pennsylvania
摘要：Multivariate time series (MTS) data such as time course gene expression data in genomics are often collected to study the dynamic nature of the systems. These data provide important information about the causal dependency among a set of random variables. In this paper, we introduce a computationally efficient algorithm to learn directed acyclic graphs (DAGs) based on MTS data, focusing on learning the local structure of a given target variable. Our algorithm is based on learning all parents (P...
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作者：Hill, Jennifer; Su, Yu-Sung
作者单位：New York University; Tsinghua University
摘要：Causal inference in observational studies typically requires making comparisons between groups that are dissimilar. For instance, researchers investigating the role of a prolonged duration of breastfeeding on child outcomes may be forced to make comparisons between women with substantially different characteristics on average. In the extreme there may exist neighborhoods of the covariate space where there are not sufficient numbers of both groups of women (those who breastfed for prolonged per...
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作者：Yuan, Ying; Zhu, Hongtu; Styner, Martin; Gilmore, John H.; Marron, J. S.
作者单位：St Jude Children's Research Hospital; University of North Carolina; University of North Carolina Chapel Hill
摘要：Diffusion tensor imaging provides important information on tissue structure and orientation of fiber tracts in brain white matter in vivo. It results in diffusion tensors, which are 3 x 3 symmetric positive definite (SPD) matrices, along fiber bundles. This paper develops a functional data analysis framework to model diffusion tensors along fiber tracts as functional data in a Riemannian manifold with a set of covariates of interest, such as age and gender. We propose a statistical model with ...
