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作者：Zhu, Bin; Taylor, Jeremy M. G.; Song, Peter X. -K.
作者单位：Duke University; Duke University; University of Michigan System; University of Michigan
摘要：In longitudinal biomedical studies, there is often interest in the rate functions, which describe the functional rates of change of biomarker profiles. This article proposes a semiparametric approach to model these functions as the realizations of stochastic processes defined by stochastic differential equations. These processes are dependent on the covariates of interest and vary around a specified parametric function. An efficient Markov chain Monte Carlo algorithm is developed for inference...
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作者：Delgado, Miguel A.; Velasco, Carlos
作者单位：Universidad Carlos III de Madrid
摘要：We propose an asymptotically distribution-free transform of the sample autocorrelations of residuals in general parametric time series models, possibly nonlinear in variables. The residuals autocorrelation function is the basic model checking tool in time series analysis, but it is not useful when its distribution is incorrectly approximated because the effects of parameter estimation and/or higher-order serial dependence have not been taken into account. The limiting distribution of the resid...
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作者：Chen, Kun; Chen, Kehui; Mueller, Hans-Georg; Wang, Jane-Ling
作者单位：Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute; University of California System; University of California Davis
摘要：We propose stringing, a class of methods where one views high-dimensional observations as functional data. Stringing takes advantage of the high dimension by representing such data as discretized and noisy observations that originate from a hidden smooth stochastic process. Assuming that the observations result from scrambling the original ordering of the observations of the process, stringing reorders the components of the high-dimensional vectors, followed by transforming the high-dimensiona...
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作者：Woodard, Dawn B.; Goldszmidt, Moises
作者单位：Cornell University; Microsoft
摘要：Large-scale distributed computing systems can suffer from occasional severe violation of performance goals; due to the complexity of these systems, manual diagnosis of the cause of the crisis is too slow to inform interventions taken during the crisis. Rapid automatic recognition of the recurrence of a problem can lead to cause diagnosis and informed intervention. We frame this as an online clustering problem, where the labels (causes) of some of the previous crises may be known. We give a fas...
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作者：Park, Cheolwoo; Ahn, Jeongyoun; Hendry, Martin; Jang, Woncheol
作者单位：University System of Georgia; University of Georgia; University of Glasgow; University System of Georgia; University of Georgia
摘要：In astronomy the study of variable stars that is, stars characterized by showing significant variation in their brightness over time has made crucial contributions to our understanding of many phenomena, from stellar birth and evolution to the calibration of the extragalactic distance scale. In this article, we develop a method for analyzing multiple, (pseudo)-periodic time series with the goal of detecting temporal trends in their periods. We allow for nonstationary noise and for clustering a...
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作者：Storelvmo, T.; Leirvik, T.
作者单位：Yale University; Universita della Svizzera Italiana
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作者：Efromovich, Sam
摘要：Nonparametric regression with predictors missing at random (MAR), where the probability of missing depends only on observed variables, is considered. Univariate predictor is the primary case of interest. A new adaptive orthogonal series estimator is developed. Large sample theory shows that the estimator is rate-minimax and it is also sharp-minimax whenever predictors are missing completely at random (MCAR). Furthermore, confidence bands, estimation of nuisance functions, including conditional...
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作者：Ghosal, Subhashis; Roy, Anindya
作者单位：North Carolina State University; University System of Maryland; University of Maryland Baltimore County
摘要：We present a flexible framework for predicting error measures in multiple testing situations under dependence. Our approach is based on modeling the distribution of the probit transform of the p-values by mixtures of multivariate skew-normal distributions. The model can incorporate dependence among p-values and it allows for shape restrictions on the p-value density. A nonparametric Bayesian scheme for estimating the components of the mixture model is outlined and Markov chain Monte Carlo algo...
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作者：Fan, Jianqing; Feng, Yang; Song, Rui
作者单位：Princeton University; Columbia University; Colorado State University System; Colorado State University Fort Collins
摘要：A variable screening procedure via correlation learning was proposed by Fan and Lv (2008) to reduce dimensionality in sparse ultra-high-dimensional models. Even when the true model is linear, the marginal regression can be highly nonlinear. To address this issue, we further extend the correlation learning to marginal nonparametric learning. Our nonparametric independence screening (NIS) is a specific type of sure independence screening. We propose several closely related variable screening pro...
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作者：Gile, Krista J.
作者单位：University of Massachusetts System; University of Massachusetts Amherst
摘要：Respondent-driven sampling is a form of link-tracing network sampling, which is widely used to study hard-to-reach populations, often to estimate population proportions. Previous treatments of this process have used a with-replacement approximation, which we show induces bias in estimates for large sample fractions and differential network connectedness by characteristic of interest. We present a treatment of respondent-driven sampling as a successive sampling process. Unlike existing represen...
