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作者：Padilla, Oscar Hernan Madrid; Athey, Alex; Reinhart, Alex; Scott, James G.
作者单位：University of California System; University of California Berkeley; University of Texas System; University of Texas Austin; Carnegie Mellon University; University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin
摘要：We propose a sequential nonparametric test for detecting a change in distribution, based on windowed Kolmogorov-Smirnov statistics. The approach is simple, robust, highly computationally efficient, easy to calibrate, and requires no parametric assumptions about the underlying null and alternative distributions. We show that both the false-alarm rate and the power of our procedure are amenable to rigorous analysis, and that the method outperforms existing sequential testing procedures in practi...
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作者：Wang, Yixin; Blei, David M.
作者单位：Columbia University; Columbia University
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作者：Geng, Junxian; Bhattacharya, Anirban; Pati, Debdeep
作者单位：State University System of Florida; Florida State University; Texas A&M University System; Texas A&M University College Station
摘要：A fundamental problem in network analysis is clustering the nodes into groups which share a similar connectivity pattern. Existing algorithms for community detection assume the knowledge of the number of clusters or estimate it a priori using various selection criteria and subsequently estimate the community structure. Ignoring the uncertainty in the first stage may lead to erroneous clustering, particularly when the community structure is vague. We instead propose a coherent probabilistic fra...
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作者：Chen, Qingxia; Harrell, Frank E., Jr.
作者单位：Vanderbilt University
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作者：Wang, HaiYing; Yang, Min; Stufken, John
作者单位：University of Connecticut; University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; Arizona State University; Arizona State University-Tempe
摘要：Extraordinary amounts of data are being produced in many branches of science. Proven statistical methods are no longer applicable with extraordinary large datasets due to computational limitations. A critical step in big data analysis is data reduction. Existing investigations in the context of linear regression focus on subsampling-based methods. However, not only is this approach prone to sampling errors, it also leads to a covariance matrix of the estimators that is typically bounded from b...
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作者：Zhou, Tingting; Elliott, Michael R.; Little, Roderick J. A.
作者单位：University of Michigan System; University of Michigan
摘要：Valid causal inference from observational studies requires controlling for confounders. When time-dependent confounders are present that serve as mediators of treatment effects and affect future treatment assignment, standard regression methods for controlling for confounders fail. Similar issues also arise in trials with sequential randomization, when randomization at later time points is based on intermediate outcomes from earlier randomized assignments. We propose a robust multiple imputati...
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作者：Athey, Susan; Imbens, Guido W.; Pollmann, Michael
作者单位：Stanford University; National Bureau of Economic Research; Stanford University
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作者：Bojinov, Iavor; Shephard, Neil
作者单位：Harvard University; Harvard University
摘要：We define causal estimands for experiments on single time series, extending the potential outcome framework to dealing with temporal data. Our approach allows the estimation of a broad class of these estimands and exact randomization-based p-values for testing causal effects, without imposing stringent assumptions. We further derive a general central limit theorem that can be used to conduct conservative tests and build confidence intervals for causal effects. Finally, we provide three methods...
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作者：Pan, Yuqing; Mai, Qing; Zhang, Xin
作者单位：State University System of Florida; Florida State University
摘要：In contemporary scientific research, it is often of great interest to predict a categorical response based on a high-dimensional tensor (i.e., multi-dimensional array) and additional covariates. Motivated by applications in science and engineering, we propose a comprehensive and interpretable discriminant analysis model, called the CATCH model (short for covariate-adjusted tensor classification in high-dimensions). The CATCH model efficiently integrates the covariates and the tensor to predict...
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作者：Pfister, Niklas; Buehlmann, Peter; Peters, Jonas
作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Copenhagen
摘要：We investigate the problem of inferring the causal predictors of a response Y from a set of d explanatory variables (X-1, ..., X-d). Classical ordinary least-square regression includes all predictors that reduce the variance of Y. Using only the causal predictors instead leads to models that have the advantage of remaining invariant under interventions; loosely speaking they lead to invariance across different environments or heterogeneity patterns. More precisely, the conditional distribution...
