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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...
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作者：Antoniano-Villalobos, Isadora; Wade, Sara; Walker, Stephen G.
作者单位：Bocconi University; University of Cambridge; University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin
摘要：Hippocampal volume is one of the best established biomarkers for Alzheimer's disease. However, for appropriate use in clinical trials research, the evolution of hippocampal volume needs to be well understood. Recent theoretical models propose a sigmoidal pattern for its evolution. To support this theory, the use of Bayesian nonparametric regression mixture models seems particularly suitable due to the flexibility that models of this type can achieve and the unsatisfactory predictive properties...
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作者：Li, Ruosha; Cheng, Yu; Fine, Jason P.
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; University of North Carolina; University of North Carolina Chapel Hill
摘要：It is often important to study the association between two continuous variables. In this work, we propose a novel regression framework for assessing conditional associations on quantiles. We develop general methodology which permits covariate effects on both the marginal quantile models for the two variables and their quantile associations. The proposed quantile copula models have straightforward interpretation, facilitating a comprehensive view of association structure which is much richer th...
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作者：Zhou, Yong-Dao; Xu, Hongquan
作者单位：Sichuan University; University of California System; University of California Los Angeles
摘要：Fractional factorial designs are widely used in various scientific investigations and industrial applications. Level permutation of factors could alter their geometrical structures and statistical properties. This article studies space-filling properties of fractional factorial designs under two commonly used space-filling measures, discrepancy and maximin distance. When all possible level permutations are considered, the average discrepancy is expressed as a linear combination of generalized ...
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作者：Lei, Jing
作者单位：Carnegie Mellon University
摘要：This article studies global testing of the slope function in functional linear regression models. A major challenge in functional global testing is to choose the dimension of projection when approximating the functional regression model by a finite dimensional multivariate linear regression model. We develop a new method that simultaneously tests the slope vectors in a sequence of functional principal components regression models. The sequence of models being tested is determined by the sample...
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作者：Lijoi, Antonio; Nipoti, Bernardo
作者单位：University of Pavia; University of Turin; Collegio Carlo Alberto
摘要：Mixture models for hazard rate functions are widely used tools for addressing the statistical analysis of survival data subject to a censoring mechanism. The present article introduced a new class of vectors of random hazard rate functions that are expressed as kernel mixtures of dependent completely random measures. This leads to define dependent nonparametric prior processes that are suitably tailored to draw inferences in the presence of heterogenous observations. Besides its flexibility, a...
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作者：Huang, Hui; Ma, Xiamei; Waagepetersen, Rasmus; Holford, Theodore R.; Wang, Rong; Risch, Harvey; Mueller, Lloyd; Guan, Yongtao
作者单位：University of Miami; Yale University; Aalborg University
摘要：We propose a novel two-step procedure to combine epidemiological data obtained from diverse sources with the aim to quantify risk factors affecting the probability that an individual develops certain disease such as cancer. In the first step, we derive all possible unbiased estimating functions based on a group of cases and a group of controls each time. In the second step, we combine these estimating functions efficiently to make full use of the information contained in data. Our approach is ...
