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作者：Zhang, Xiaoke; Park, Byeong U.; Wang, Jane-Ling
作者单位：University of California System; University of California Davis; Seoul National University (SNU)
摘要：The additive model is an effective dimension-reduction approach that also provides flexibility in modeling the relation between a response variable and key covariates. The literature is largely developed to scalar response and vector covariates. In this article, more complex data are of interest, where both the response and the covariates are functions. We propose a functional additive model together with a new backfitting algorithm to estimate the unknown regression functions, whose component...
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作者：Blei, David M.
作者单位：Princeton University
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作者：Grimmer, Justin
作者单位：Stanford University
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作者：Taddy, Matt
作者单位：University of Chicago
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作者：Guerrier, Stephane; Skaloud, Jan; Stebler, Yannick; Victoria-Feser, Maria-Pia
作者单位：University of Geneva; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
摘要：This article presents a new estimation method for the parameters of a time series model. We consider here composite Gaussian processes that are the sum of independent Gaussian processes which, in turn, explain an important aspect of the time series, as is the case in engineering and natural sciences. The proposed estimation method offers an alternative to classical estimation based on the likelihood, that is straightforward to implement and often the only feasible estimation method with comple...
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作者：Ma, Yanyuan; Kim, Mijeong; Genton, Marc G.
作者单位：Texas A&M University System; Texas A&M University College Station; Texas A&M University System; Texas A&M University College Station; King Abdullah University of Science & Technology
摘要：We propose semiparametric methods to estimate the center and shape of a symmetric population when a representative sample of the population is unavailable due to selection bias. We allow an arbitrary sample selection mechanism determined by the data collection procedure, and we do not impose any parametric form on the population distribution. Under this general framework, we construct a family of consistent estimators of the center that is robust to population model misspecification, and we id...
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作者：Morrissette, Jason L.; McDermott, Michael P.
作者单位：University of Rochester
摘要：When interactions are identified in analysis of covariance models, it becomes important to identify values of the covariates for which there are significant differences or, more generally, significant contrasts among the group mean responses. Inferential procedures that incorporate a priori order restrictions among the group mean responses would be expected to be superior to those that ignore this information. In this article, we focus on analysis of covariance models with prespecified order r...
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作者：Rougier, Jonathan; Goldstein, Michael; House, Leanna
作者单位：University of Bristol; Durham University; Virginia Polytechnic Institute & State University
摘要：The challenge of understanding complex systems often gives rise to a multiplicity of models. It is natural to consider whether the outputs of these models can be combined to produce a system prediction that is more informative than the output of any one of the models taken in isolation. And, in particular, to consider the relationship between the spread of model outputs and system uncertainty. We describe a statistical framework for such a combination, based on the exchangeability of the model...
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作者：Yu, Tao; Li, Pengfei
作者单位：National University of Singapore; University of Waterloo
摘要：Diffusion tensor imaging (DTI), based on the diffusion-weighted imaging (DWI) data acquired from magnetic resonance experiments, has been widely used to analyze the physical structure of white-matter fibers in the human brain in vivo. The raw DWI data, however, carry noise; this contaminates the diffusion tensor (DT) estimates and introduces systematic bias into the induced eigenvalues. These bias components affect the effectiveness of fiber-tracking algorithms. In this article, we propose a t...
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作者：Raymer, James; Wisniowski, Arkadiusz; Forster, Jonathan J.; Smith, Peter W. F.; Bijak, Jakub
作者单位：University of Southampton
摘要：International migration data in Europe are collected by individual countries with separate collection systems and designs. As a result, reported data are inconsistent in availability, definition, and quality. In this article, we propose a Bayesian model to overcome the limitations of the various data sources. The focus is on estimating recent international migration flows among 31 countries in the European Union and European Free Trade Association from 2002 to 2008, using data collated by Euro...
