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作者：Li, Fan; Zhang, Nancy R.
作者单位：Duke University; Stanford University
摘要：We consider the problem of variable selection in regression modeling in high-dimensional spaces where there is known structure among the covariates. This is an unconventional variable selection problem for two reasons: (1) The dimension of the covariate space is comparable, and often much larger, than the number of subjects in the study. and (2) the covariate space is highly structured, and in some cases it is desirable to incorporate this structural information in to the model building proces...
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作者：Li, Lexin; Li, Bing; Zhu, Li-Xing
作者单位：North Carolina State University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Hong Kong Baptist University
摘要：In many regression applications, the predictors fall naturally into a number of groups or domains, and it is often desirable to establish a domain-specific relation between the predictors and the response. In this article, we consider dimension reduction that incorporates such domain knowledge. The proposed method is based on the derivative of the conditional mean, where the differential operator is constrained to the form of a direct sum. This formulation also accommodates the situations wher...
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作者：Freyermuth, Jean-Marc; Ombao, Hernando; von Sachs, Rainer
作者单位：Universite Catholique Louvain; Brown University
摘要：This article develops a method for estimating the spectrum of a stationary process using time series traces recorded from experimental designs. Our procedure estimates the common log-spectrum and the variability over the traces (or subjects) using a mixed effects model. We combine spatially adaptive smoothing methods with recursive dyadic partitioning to construct a model for predicting subject-specific effects. The method is easy to implement and can handle large datasets because it uses the ...
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作者：Paparoditis, Efstathios
作者单位：University of Cyprus
摘要：We propose a simple and powerful procedure to validate the assumption of weak stationarity in time series analysis. Our focus is on processes with a slowly varying autocovariance structure. The procedure evaluates the supremum over time of the L-2-distance between the local sample spectral density (local periodogram) calculated using a segment of observations falling within a rolling window and an estimator of the spectral density obtained using the entire time series at hand. Large sample pro...
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作者：Ren, Lu; Dunson, David; Lindroth, Scott; Carin, Lawrence
作者单位：Duke University; Duke University; Duke University
摘要：The dynamic hierarchical Dirichlet process (dHDP) is developed to model complex sequential data, with a focus on audio signals from music. The music is represented in terms of a sequence of discrete observations, and the sequence is modeled using a hidden Markov model (HMM) with time-evolving parameters. The dHDP imposes the belief that observations that are temporally proximate are more likely to be drawn from HMMs with similar parameters, while also allowing for innovation associated with ab...
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作者：Chen, Baojiang; Yi, Grace Y.; Cook, Richard J.
作者单位：University of Washington; University of Washington Seattle; University of Waterloo
摘要：Longitudinal studies of ten feature incomplete response and covariate data It is well known that biases can arise from naive analyses of available data. but the precise impact of Incomplete data depends on the frequency of missing data and the strength of the association between the response variables and emanates and the missing-data indicators Various factors may influence the availability of response and covariate data at scheduled assessment times, and at any given assessment time the resp...
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作者：Ghosh, Sujit K.; Bhave, Prakash V.; Davis, Jerry M.; Lee, Hyeyoung
作者单位：North Carolina State University; North Carolina State University
摘要：Atmospheric concentrations of total nitrate (TNO3), defined here as gas-phase nitric acid plus particle-phase nitrate, are difficult to simulate in numerical air quality models due to the presence of a variety of formation pathways and loss mechanisms, some of which are highly uncertain. The goal of this study is to estimate the relative importance of these different pathways across the Eastern United States by identifying empirical relationships that exist between TNO3 concentrations and a se...
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作者：Ver Hoef, Jay M.; Peterson, Erin E.
作者单位：National Oceanic Atmospheric Admin (NOAA) - USA; Commonwealth Scientific & Industrial Research Organisation (CSIRO)
摘要：In this article we use moving averages to develop new classes of models in a flexible modeling framework for stream networks Streams and rivers are among our most important resources, yet models with autocorrelated errors for spatially continuous stream networks have been described only recently We develop models based on stream distance rather than on Euclidean distance Spatial autocovariance models developed for Euclidean distance may not be valid when using stream distance We begin by descr...
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作者：Gabrys, Robertas; Horvath, Lajos; Kokoszka, Piotr
作者单位：Utah System of Higher Education; Utah State University; Utah System of Higher Education; University of Utah
摘要：The paper proposes two inferential tests for error correlation in the functional linear model, which complement the available graphical goodness-of-fit checks. To construct them, finite dimensional residuals are computed in two different ways, and then their autocorrelations are suitably defined. From these autocorrelation matrices, two quadratic forms are constructed whose limiting distribution are chi-squared with known numbers of degrees of freedom (different for the two forms). The asympto...
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作者：Witten, Daniela M.; Tibshirani, Robert
作者单位：Stanford University; Stanford University
摘要：We consider the problem of clustering observations using a potentially large set of features. One might expect that the true underlying clusters present in the data differ only with respect to a small fraction of the features, and will be missed if one clusters the observations using the full set of features. We propose a novel framework for sparse clustering, in which one clusters the observations using an adaptively chosen subset of the features. The method uses a lasso-type penalty to selec...
