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作者：Ghassami, Amiremad; Yang, Alan; Shpitser, Ilya; Tchetgen, Eric Tchetgen
作者单位：Boston University; Stanford University; Johns Hopkins University; University of Pennsylvania
摘要：Proximal causal inference was recently proposed as a framework to identify causal effects from observational data in the presence of hidden confounders for which proxies are available. In this paper, we extend the proximal causal inference approach to settings where identification of causal effects hinges upon a set of mediators that are not observed, yet error prone proxies of the hidden mediators are measured. Specifically, (i) we establish causal hidden mediation analysis, which extends cla...
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作者：Zhu, Changbo; Yao, Junwen; Wang, Jane-Ling
作者单位：University of Notre Dame; University of California System; University of California Davis
摘要：With the advance of science and technology, more and more data are collected in the form of functions. A fundamental question for a pair of random functions is to test whether they are independent. This problem becomes quite challenging when the random trajectories are sampled irregularly and sparsely for each subject. In other words, each random function is only sampled at a few time-points, and these time-points vary with subjects. Furthermore, the observed data may contain noise. To the bes...
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作者：Bai, Lujia; Wu, Weichi
作者单位：Tsinghua University; Tsinghua University
摘要：Long-run covariance matrix estimation is the building block of time series inference. The corresponding difference-based estimator, which avoids detrending, has attracted considerable interest due to its robustness to both smooth and abrupt structural breaks and its competitive finite sample performance. However, existing methods mainly focus on estimators for the univariate process, while their direct and multivariate extensions for most linear models are asymptotically biased. We propose a n...
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作者：Wang, Yuyao; Ying, Andrew; Xu, Ronghui
作者单位：University of California System; University of California San Diego; University of Pennsylvania
摘要：In prevalent cohort studies with follow-up, the time-to-event outcome is subject to left truncation leading to selection bias. For estimation of the distribution of the time to event, conventional methods adjusting for left truncation tend to rely on the quasi-independence assumption that the truncation time and the event time are independent on the observed region. This assumption is violated when there is dependence between the truncation time and the event time, possibly induced by measured...
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作者：Han, Ruijian; Luo, Lan; Lin, Yuanyuan; Huang, Jian
作者单位：Hong Kong Polytechnic University; Rutgers University System; Chinese University of Hong Kong
摘要：We propose a debiased stochastic gradient descent algorithm for online statistical inference with high-dimensional data. Our approach combines the debiasing technique developed in high-dimensional statistics with the stochastic gradient descent algorithm. It can be used to construct confidence intervals efficiently in an online fashion. Our proposed algorithm has several appealing aspects: as a one-pass algorithm, it reduces the time complexity; in addition, each update step requires only the ...
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作者：Samyak, Rajanala; Palacios, Julia A.
作者单位：Stanford University
摘要：Rooted and ranked phylogenetic trees are mathematical objects that are useful in modelling hierarchical data and evolutionary relationships with applications to many fields such as evolutionary biology and genetic epidemiology. Bayesian phylogenetic inference usually explores the posterior distribution of trees via Markov chain Monte Carlo methods. However, assessing uncertainty and summarizing distributions remains challenging for these types of structures. While labelled phylogenetic trees h...
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作者：Calissano, Anna; Feragen, Aasa; Vantini, Simone
作者单位：Polytechnic University of Milan; Technical University of Denmark
摘要：Statistical analysis for populations of networks is widely applicable, but challenging, as networks have strongly non-Euclidean behaviour. Graph space is an exhaustive framework for studying populations of unlabelled networks that are weighted or unweighted, uni- or multilayered, directed or undirected. Viewing graph space as the quotient of a Euclidean space with respect to a finite group action, we show that it is not a manifold, and that its curvature is unbounded from above. Within this ge...
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作者：Auerbach, Eric; Auerbach, Jonathan; Tabord-Meehan, Max
作者单位：Northwestern University; George Mason University; University of Chicago
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作者：Li, Canhui; Zeng, Donglin; Zhu, Wensheng
作者单位：Northeast Normal University - China; Northeast Normal University - China; University of Michigan System; University of Michigan; Yunnan University
摘要：One of the most important problems in precision medicine is to find the optimal individualized treatment rule, which is designed to recommend treatment decisions and maximize overall clinical benefit to patients based on their individual characteristics. Typically, the expected clinical outcome is required to be estimated first, for which an outcome regression model or a propensity score model usually needs to be assumed with most existing statistical methods. However, if either model assumpti...
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作者：Kono, Haruki
作者单位：Massachusetts Institute of Technology (MIT)
摘要：We explore how much knowing a parametric restriction on propensity scores improves semiparametric efficiency bounds in the potential outcome framework. For stratified propensity scores, considered as a parametric model, we derive explicit formulas for the efficiency gain from knowing how the covariate space is split. Based on these, we find that the efficiency gain decreases as the partition of the stratification becomes finer. For general parametric models, where it is hard to obtain explicit...
