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作者：Xu, Chen; Chen, Jiahua
作者单位：University of British Columbia
摘要：Feature selection is fundamental for modeling the high-dimensional data, where the number of features can be huge and much larger than the sample size. Since the feature space is so large, many traditional procedures become numerically infeasible. It is hence essential to first remove most apparently noninfluential features before any elaborative analysis. Recently, several procedures have been developed for this purpose, which include the sure-independent-screening (SIS) as a widely used tech...
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作者：Li, Bing; Chun, Hyonho; Zhao, Hongyu
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Purdue University System; Purdue University; Yale University
摘要：We introduce a nonparametric method for estimating non-Gaussian graphical models based on a new statistical relation called additive conditional independence, which is a three-way relation among random vectors that resembles the logical structure of conditional independence. Additive conditional independence allows us to use one-dimensional kernel regardless of the dimension of the graph, which not only avoids the curse of dimensionality but also simplifies computation. It also gives rise to a...
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作者：Aue, Alexander; Cheung, Rex C. Y.; Lee, Thomas C. M.; Zhong, Ming
作者单位：University of California System; University of California Davis
摘要：This article proposes new model-fitting techniques for quantiles of an observed data sequence, including methods for data segmentation and variable selection. The main contribution, however, is in providing a means to perform these two tasks simultaneously. This is achieved by matching the data with the best-fitting piecewise quantile regression model, where the fit is determined by a penalization derived from the minimum description length principle. The resulting optimization problem is solv...
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作者：Efron, Bradley
作者单位：Stanford University
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作者：Politis, Dimitris N.
作者单位：University of California System; University of California San Diego
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作者：Deng, Ke; Han, Simeng; Li, Kate J.; Liu, Jun S.
作者单位：Tsinghua University; Harvard University; Suffolk University
摘要：Rank aggregation, that is, combining several ranking functions (called base rankers) to get aggregated, usually stronger rankings of a given set of items, is encountered in many disciplines. Most methods in the literature assume that base rankers of interest are equally reliable. It is very common in practice, however, that some rankers are more informative and reliable than others. It is desirable to distinguish high quality base rankers from low quality ones and treat them differently. Some ...
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作者：Hallin, Marc; Paindaveine, Davy; Verdebout, Thomas
作者单位：Universite Libre de Bruxelles; Princeton University; Universite Libre de Bruxelles; Universite de Lille; Inria; Universite de Lille
摘要：We propose rank-based estimators of principal components, both in the one-sample and, under the assumption of common principal components, in the m-sample cases. Those estimators are obtained via a rank-based version of Le Cam's one-step method, combined with an estimation of cross-information quantities. Under arbitrary elliptical distributions with, in the m-sample case, possibly heterogeneous radial densities, those R-estimators remain root-n consistent and asymptotically normal, while achi...
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作者：Sobel, Michael E.; Lindquist, Martin A.
作者单位：Columbia University; Johns Hopkins University
摘要：Functional magnetic resonance imaging (fMRI) has facilitated major advances in understanding human brain function. Neuroscientists are interested in using fMRI to study the effects of external stimuli on brain activity and causal relationships among brain regions, but have not stated what is meant by causation or defined the effects they purport to estimate. Building on Rubin's causal model, we construct a framework for causal inference using blood oxygenation level dependent (BOLD) fMRI time ...
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作者：Chen, Tianle; Wang, Yuanjia; Chen, Huaihou; Marder, Karen; Zeng, Donglin
作者单位：Columbia University; New York University; Columbia University; Columbia University; University of North Carolina; University of North Carolina Chapel Hill
摘要：We develop methods to accurately predict whether presymptomatic individuals are at risk of a disease based on their various marker profiles, which offers an opportunity for early intervention well before definitive clinical diagnosis. For many diseases, existing clinical literature may suggest the risk of disease varies with some markers of biological and etiological importance, for example, age. To identify effective prediction rules using nonparametric decision functions, standard statistica...
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作者：Herbei, Radu; Berliner, L. Mark
作者单位：University System of Ohio; Ohio State University
摘要：We provide a Bayesian analysis of ocean circulation based on data collected in the South Atlantic Ocean. The analysis incorporates a reaction-diffusion partial differential equation that is not solvable in closed form. This leads to an intractable likelihood function. We describe a novel Markov chain Monte Carlo approach that does not require a likelihood evaluation. Rather, we use unbiased estimates of the likelihood and a Bernoulli factory to decide whether or not proposed states are accepte...
