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作者：Lin, Lifeng
作者单位：University of Arizona
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作者：Bhattacharyya, Rupam; Henderson, Nicholas C.; Baladandayuthapani, Veerabhadran
作者单位：University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
摘要：Rapid advancements in collection and dissemination of multi-platform molecular and genomics data has resulted in enormous opportunities to aggregate such data in order to understand, prevent, and treat human diseases. While significant improvements have been made in multi-omic data integration methods to discover biological markers and mechanisms underlying both prognosis and treatment, the precise cellular functions governing these complex mechanisms still need detailed and data-driven de-nov...
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作者：Cappello, Lorenzo; Veber, Amandine; Palacios, Julia A.
作者单位：Pompeu Fabra University; Barcelona School of Economics; Centre National de la Recherche Scientifique (CNRS); Universite Paris Cite; Stanford University
摘要：Molecular sequence variation at a locus informs about the evolutionary history of the sample and past population size dynamics. The Kingman coalescent is used in a generative model of molecular sequence variation to infer evolutionary parameters. However, it is well understood that inference under this model does not scale well with sample size. Here, we build on recent work based on a lower resolution coalescent process, the Tajima coalescent, to model longitudinal samples. While the Kingman ...
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作者：Gnecco, Nicola; Terefe, Edossa Merga; Engelke, Sebastian
作者单位：University of Copenhagen; University of Geneva; Hawassa University
摘要：Classical methods for quantile regression fail in cases where the quantile of interest is extreme and only few or no training data points exceed it. Asymptotic results from extreme value theory can be used to extrapolate beyond the range of the data, and several approaches exist that use linear regression, kernel methods or generalized additive models. Most of these methods break down if the predictor space has more than a few dimensions or if the regression function of extreme quantiles is co...
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作者：Wang, Xueqin; Zhu, Jin; Pan, Wenliang; Zhu, Junhao; Zhang, Heping
作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; Sun Yat Sen University; University of London; London School Economics & Political Science; Chinese Academy of Sciences; Yale University; University of Toronto
摘要：The distribution function is essential in statistical inference and connected with samples to form a directed closed loop by the correspondence theorem in measure theory and the Glivenko-Cantelli and Donsker properties. This connection creates a paradigm for statistical inference. However, existing distribution functions are defined in Euclidean spaces and are no longer convenient to use in rapidly evolving data objects of complex nature. It is imperative to develop the concept of the distribu...
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作者：Shen, Ye; Cai, Hengrui; Song, Rui
作者单位：North Carolina State University; University of California System; University of California Irvine
摘要：Evaluating the performance of an ongoing policy plays a vital role in many areas such as medicine and economics, to provide crucial instructions on the early-stop of the online experiment and timely feedback from the environment. Policy evaluation in online learning thus attracts increasing attention by inferring the mean outcome of the optimal policy (i.e., the value) in real-time. Yet, such a problem is particularly challenging due to the dependent data generated in the online environment, t...
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作者：Huang, Zhen; Sen, Bodhisattva
作者单位：Columbia University
摘要：Given M >= 2 distributions defined on a general measurable space, we introduce a nonparametric (kernel) measure of multi-sample dissimilarity (KMD)-a parameter that quantifies the difference between the M distributions. The population KMD, which takes values between 0 and 1, is 0 if and only if all the M distributions are the same, and 1 if and only if all the distributions are mutually singular. Moreover, KMD possesses many properties commonly associated with f-divergences such as the data pr...
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作者：Shi, Chenlu; Xu, Hongquan
作者单位：Colorado State University System; Colorado State University Fort Collins; University of California System; University of California Los Angeles
摘要：Computer experiments call for space-filling designs. Recently, a minimum aberration type space-filling criterion was proposed to rank and assess a family of space-filling designs including Latin hypercubes and strong orthogonal arrays. It aims at capturing the space-filling properties of a design when projected onto subregions of various sizes. In this article, we also consider the dimension aside from the sizes of subregions by proposing first an expanded space-filling hierarchy principle and...
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作者：Harshaw, Christopher; Saevje, Fredrik; Spielman, Daniel A.; Zhang, Peng
作者单位：Massachusetts Institute of Technology (MIT); Yale University; Rutgers University System; Rutgers University New Brunswick
摘要：The design of experiments involves a compromise between covariate balance and robustness. This article provides a formalization of this tradeoff and describes an experimental design that allows experimenters to navigate it. The design is specified by a robustness parameter that bounds the worst-case mean squared error of an estimator of the average treatment effect. Subject to the experimenter's desired level of robustness, the design aims to simultaneously balance all linear functions of pote...
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作者：Choi, David
作者单位：Carnegie Mellon University
摘要：In experiments that study social phenomena, such as peer influence or herd immunity, the treatment of one unit may influence the outcomes of others. Such interference between units violates traditional approaches for causal inference, so that additional assumptions are often imposed to model or limit the underlying social mechanism. For binary outcomes, we propose new estimands that can be estimated without such assumptions, allowing for interval estimates that assume only the randomization of...
