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作者：Zhu, Bin; Dunson, David B.
作者单位：National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); NIH National Cancer Institute- Division of Cancer Epidemiology & Genetics; Duke University
摘要：We propose a nested Gaussian process (nGP) as a locally adaptive prior for Bayesian nonparametric regression. Specified through a set of stochastic differential equations (SDEs), the nGP imposes a Gaussian process prior for the function's mth-order derivative. The nesting comes in through including a local instantaneous mean function, which is drawn from another Gaussian process inducing adaptivity to locally varying smoothness. We discuss the support of the nGP prior in terms of the closure o...
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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：Cai, Tony; Liu, Weidong; Xia, Yin
作者单位：University of Pennsylvania; Shanghai Jiao Tong University; Shanghai Jiao Tong University
摘要：In the high-dimensional setting, this article considers three interrelated problems: (a) testing the equality of two covariance matrices Sigma(1) and Sigma(2); (b) recovering the support of Sigma(1) - Sigma(2); and (c) testing the equality of Sigma(1) and Sigma(2) row by row. We propose a new test for testing the hypothesis H-0: Sigma(1) = Sigma(2) and investigate its theoretical and numerical properties. The limiting null distribution of the test statistic is derived and the power of the test...
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作者：Lei, Jing; Robins, James; Wasserman, Larry
作者单位：Carnegie Mellon University; Harvard University; Harvard University; Carnegie Mellon University
摘要：This article introduces a new approach to prediction by bringing together two different nonparametric ideas: distribution-free inference and nonparametric smoothing. Specifically, we consider the problem of constructing nonparametric tolerance/prediction sets. We start from the general conformal prediction approach, and we use a kernel density estimator as a measure of agreement between a sample point and the underlying distribution. The resulting prediction set is shown to be closely related ...
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作者：Hoshino, Takahiro
作者单位：Nagoya University
摘要：We propose a new semiparametric Bayesian model for causal inference in which assignment to treatment depends on potential outcomes. The model uses the probit stick-breaking process mixture proposed by Chung and Dunson (2009), a variant of the Dirichlet process mixture modeling. In contrast to previous Bayesian models, the proposed model directly estimates the parameters of the marginal parametric model of potential outcomes, while it relaxes the strong ignorability assumption, and requires no ...
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作者：Li, Yehua; Wang, Naisyin; Carroll, Raymond J.
作者单位：Iowa State University; University of Michigan System; University of Michigan; Texas A&M University System; Texas A&M University College Station
摘要：Functional principal component analysis (FPCA) has become the most widely used dimension reduction tool for functional data analysis. We consider functional data measured at random, subject-specific time points, contaminated with measurement error, allowing for both sparse and dense functional data, and propose novel information criteria to select the number of principal component in such data. We propose a Bayesian information criterion based on marginal modeling that can consistently select ...
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作者：Zeng, Donglin; Wang, Yuanjia
作者单位：University of North Carolina; University of North Carolina Chapel Hill; Columbia University
