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作者：Zhu, Hongxiao; Brown, Philip J.; Morris, Jeffrey S.
作者单位：University of Kent; University of Texas System; UTMD Anderson Cancer Center
摘要：Functional data are increasingly encountered in scientific studies, and their high dimensionality and complexity lead to many analytical challenges. Various methods for functional data analysis have been developed, including functional response regression methods that involve regression of a functional response on univariate/multivariate predictors with nonparametrically represented functional coefficients. In existing methods, however, the functional regression can be sensitive to outlying cu...
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作者：Zantedeschi, Daniel; Damien, Paul; Polson, Nicholas G.
作者单位：University of Texas System; University of Texas Austin; University of Chicago
摘要：Dynamic partition models are used to predict movements in the term structure of interest rates. This allows one to understand historic cycles in the performance of how interest rates behave, and to offer policy makers guidance regarding future expectations on their evolution. Our approach allows for a random number of possible change points in the term structure of interest rates. We use particle learning to learn about the unobserved state variables in a new class of dynamic product partition...
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作者：Datta, Gauri S.; Hall, Peter; Mandal, Abhyuday
作者单位：University System of Georgia; University of Georgia; University of Melbourne
摘要：The models used in small-area inference often involve unobservable random effects. While this can significantly improve the adaptivity and flexibility of a model, it also increases the variability of both point and interval estimators. If we could test for the existence of the random effects, and if the test were to show that they were unlikely to be present, then we would arguably not need to incorporate them into the model, and thus could significantly improve the precision of the methodolog...
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作者：Taddy, Matthew A.; Gramacy, Robert B.; Polson, Nicholas G.
作者单位：University of Chicago; University of Cambridge
摘要：Dynamic regression trees are an attractive option for automatic regression and classification with complicated response surfaces in online application settings. We create a sequential tree model whose state changes in time with the accumulation of new data, and provide particle learning algorithms that allow for the efficient online posterior filtering of tree states. A major advantage of tree regression is that it allows for the use of very simple models within each partition. The model also ...
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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
