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作者：Huang, Hui; Li, Yehua; Guan, Yongtao
作者单位：Peking University; Peking University; Iowa State University; University of Miami
摘要：In a cocaine dependence treatment study, we have paired binary longitudinal trajectories that record the cocaine use patterns of each patient before and after a treatment. To better understand the drug-using behaviors among the patients, we propose a general framework based on functional data analysis to jointly model and cluster these paired non-Gaussian longitudinal trajectories. Our approach assumes that the response variables follow distributions from the exponential family, with the canon...
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作者：Mueller, Peter; Quintana, Fernando
作者单位：University of Texas System; University of Texas Austin; Pontificia Universidad Catolica de Chile
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作者：Bar, Haim Y.; Booth, James G.; Wells, Martin T.
作者单位：University of Connecticut; Cornell University; Cornell University
摘要：We develop a novel approach for testing treatment effects in high-throughput data. Most previous works on this topic focused on testing for differences between the means, but recently it has been recognized that testing for differential variation is probably as important. We take it a step further, and introduce a bivariate model modeling strategy which accounts for both differential expression and differential variation. Our model-based approach, in which the differential mean and variance ar...
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作者：Pan, Guangming; Gao, Jiti; Yang, Yanrong
作者单位：Nanyang Technological University; Monash University
摘要：random vectors of length p in the form of a matrix, we develop a linear spectral statistic of the constructed matrix to test whether the n random vectors are independent or not. Specifically, the proposed statistic can also be applied to n random vectors, each of whose elements can be written as either a linear stationary process or a linear combination of independent random variables. The asymptotic distribution of the proposed test statistic is established for the case of 0 < lim(n ->infinit...
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作者：Claeskens, Gerda; Hubert, Mia; Slaets, Leen; Vakili, Kaveh
作者单位：KU Leuven; KU Leuven; European Organisation for Research & Treatment of Cancer
摘要：This article defines and studies a depth for multivariate functional data. By the multivariate nature and by including a weight function, it acknowledges important characteristics of functional data, namely differences in the amount of local amplitude, shape, and phase variation. We study both population and finite sample versions. The multivariate sample of curves may include warping functions, derivatives, and integrals of the original curves for a better overall representation of the functi...
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作者：Portier, Francois; Delyon, Bernard
作者单位：Universite Catholique Louvain; Universite de Rennes
摘要：To test if an unknown matrix M-0 has a given rank (null hypothesis noted H-0), we consider a statistic that is a squared distance between an estimator (M) over cap and the submanifold of fixed-rank matrix. Under H-0, this statistic converges to a weighted chi-squared distribution. We introduce the constrained bootstrap (CS bootstrap) to estimate the law of this statistic under H-0. An important point is that even if H-0 fails, the CS bootstrap reproduces the behavior of the statistic under H-0...
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作者：Farah, Marian; Birrell, Paul; Conti, Stefano; De Angelis, Daniela
作者单位：MRC Biostatistics Unit; University of Cambridge; Public Health England; Health Protection Agency
摘要：In this article, we develop a Bayesian framework for parameter estimation of a computationally expensive dynamic epidemic model using time series epidemic data. Specifically, we work with a model for A/H1N1 influenza, which is implemented as a deterministic computer simulator, taking as input the underlying epidemic parameters and calculating the corresponding time series of reported infections. To obtain Bayesian inference for the epidemic parameters, the simulator is embedded in the likeliho...
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作者：Lu, Zudi; Tjostheim, Dag
作者单位：University of Southampton; University of Southampton; University of Bergen
摘要：Nonparametric estimation of probability density functions, both marginal and joint densities, is a very useful tool in statistics. The kernel method is popular and applicable to dependent data, including time series and spatial data. But at least for the joint density, one has had to assume that data are observed at regular time intervals or on a regular grid in space. Though this is not very restrictive in the time series case, it often is in the spatial case. In fact, to a large degree it ha...
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作者：Li, Yehua; Guan, Yongtao
作者单位：Iowa State University; Iowa State University; University of Miami
摘要：In disease surveillance applications, the disease events are modeled by spatiotemporal point processes. We propose a new class of semiparametric generalized linear mixed model for such data, where the event rate is related to some known risk factors and some unknown latent random effects. We model the latent spatiotemporal process as spatially correlated functional data, and propose Poisson maximum likelihood and composite likelihood methods based on spline approximations to estimate the mean ...
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作者：Zhu, Hongtu; Fan, Jianqing; Kong, Linglong
作者单位：University of North Carolina; University of North Carolina Chapel Hill; Princeton University; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; University of Alberta
摘要：Motivated by recent work on studying massive imaging data in various neuroimaging studies, we propose a novel spatially varying coefficient model (SVCM) to capture the varying association between imaging measures in a three-dimensional volume (or two-dimensional surface) with a set of covariates. Two stylized features of neuorimaging data are the presence of multiple piecewise smooth regions with unknown edges and jumps and substantial spatial correlations. To specifically account for these tw...
