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作者：Luedtke, Alex; Carone, Marco; van der Laan, Mark J.
作者单位：University of Washington; University of Washington Seattle; Fred Hutchinson Cancer Center; University of California System; University of California Berkeley
摘要：We present a novel family of non-parametric omnibus tests of the hypothesis that two unknown but estimable functions are equal in distribution when applied to the observed data structure. We developed these tests, which represent a generalization of the maximum mean discrepancy tests described by Gretton and colleagues, using recent developments from the higher order pathwise differentiability literature. Despite their complex derivation, the associated test statistics can be expressed quite s...
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作者：Xiang, Dongdong; Zhao, Sihai Dave; Cai, T. Tony
作者单位：East China Normal University; University of Illinois System; University of Illinois Urbana-Champaign; University of Pennsylvania
摘要：The integrative analysis of multiple data sets is becoming increasingly important in many fields of research. When the same features are studied in several independent experiments, it can often be useful to analyse jointly the multiple sequences of multiple tests that result. It is frequently necessary to classify each feature into one of several categories, depending on the null and non-null configuration of its corresponding test statistics. The paper studies this signal classification probl...
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作者：Frot, Benjamin; Nandy, Preetam; Maathuis, Marloes H.
作者单位：University of Pennsylvania
摘要：We introduce a new method to estimate the Markov equivalence class of a directed acyclic graph (DAG) in the presence of hidden variables, in settings where the underlying DAG among the observed variables is sparse, and there are a few hidden variables that have a direct effect on many of the observed variables. Building on the so-called low rank plus sparse framework, we suggest a two-stage approach which first removes the effect of the hidden variables and then estimates the Markov equivalenc...
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作者：Bassett, Robert; Sharpnack, James
作者单位：United States Department of Defense; United States Navy; Naval Postgraduate School; University of California System; University of California Davis
摘要：We introduce a method for non-parametric density estimation on geometric networks. We define fused density estimators as solutions to a total variation regularized maximum likelihood density estimation problem. We provide theoretical support for fused density estimation by proving that the squared Hellinger rate of convergence for the estimator achieves the minimax bound over univariate densities of log-bounded variation. We reduce the original variational formulation to transform it into a tr...
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作者：Baranowski, Rafal; Chen, Yining; Fryzlewicz, Piotr
作者单位：University of London; London School Economics & Political Science
摘要：We propose a new, generic and flexible methodology for non-parametric function estimation, in which we first estimate the number and locations of any features that may be present in the function and then estimate the function parametrically between each pair of neighbouring detected features. Examples of features handled by our methodology include change points in the piecewise constant signal model, kinks in the piecewise linear signal model and other similar irregularities, which we also ref...
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作者：Niu, Mu; Cheung, Pokman; Lin, Lizhen; Dai, Zhenwen; Lawrence, Neil; Dunson, David
作者单位：University of Plymouth; University of Notre Dame; University of Sheffield; Amazon.com; Duke University
摘要：We propose a class of intrinsic Gaussian processes (GPs) for interpolation, regression and classification on manifolds with a primary focus on complex constrained domains or irregularly shaped spaces arising as subsets or submanifolds of R, R2, R3 and beyond. For example, intrinsic GPs can accommodate spatial domains arising as complex subsets of Euclidean space. Intrinsic GPs respect the potentially complex boundary or interior conditions as well as the intrinsic geometry of the spaces. The k...
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作者：Cai, T. Tony; Zhang, Linjun
作者单位：University of Pennsylvania
摘要：The paper develops optimality theory for linear discriminant analysis in the high dimensional setting. A data-driven and tuning-free classification rule, which is based on an adaptive constrained l(1)-minimization approach, is proposed and analysed. Minimax lower bounds are obtained and this classification rule is shown to be simultaneously rate optimal over a collection of parameter spaces. In addition, we consider classification with incomplete data under the missingness completely at random...
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作者：Li, Ang; Barber, Rina Foygel
作者单位：University of Chicago
摘要：In multiple-testing problems, where a large number of hypotheses are tested simultaneously, false discovery rate (FDR) control can be achieved with the well-known Benjamini-Hochberg procedure, which a(0,1]dapts to the amount of signal in the data, under certain distributional assumptions. Many modifications of this procedure have been proposed to improve power in scenarios where the hypotheses are organized into groups or into a hierarchy, as well as other structured settings. Here we introduc...
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作者：Piao, Jin; Ning, Jing; Shen, Yu
作者单位：University of Southern California; University of Texas System; UTMD Anderson Cancer Center
摘要：To understand better the relationship between patient characteristics and their residual survival after an intermediate event such as the local recurrence of cancer, it is of interest to identify patients with the intermediate event and then to analyse their residual survival data. One challenge in analysing such data is that the observed residual survival times tend to be longer than those in the target population, since patients who die before experiencing the intermediate event are excluded...
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作者：Chown, Justin; Muller, Ursula U.
作者单位：Ruhr University Bochum; Texas A&M University System; Texas A&M University College Station
