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作者：Jiang, Bo; Ye, Chao; Liu, Jun S.
作者单位：Harvard University; Tsinghua University; Tsinghua University; Tsinghua University; Harvard University
摘要：K-sample testing problems arise in many scientific applications and have attracted statisticians' attention for many years. We propose an omnibus nonparametric method based on an optimal discretization (aka slicing) of continuous random variables in the test. The novelty of our approach lies in the inclusion of a term penalizing the number of slices (i.e., the resolution of the discretization) so as to regularize the corresponding likelihood-ratio test statistic. An efficient dynamic programmi...
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作者：Ma, Li
作者单位：Duke University
摘要：This article shows that a probabilistic version of the classical forward-stepwise variable inclusion procedure can serve as a general data-augmentation scheme for model space distributions in (generalized) linear models. This latent variable representation takes the form of a Markov process, thereby allowing information propagation algorithms to be applied for sampling from model space posteriors. In particular, We propose a sequential Monte Carlo method for achieving effective unbiased Bayesi...
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作者：Liu, Dungang; Liu, Regina Y.; Xie, Minge
作者单位：Yale University; Rutgers University System; Rutgers University New Brunswick
摘要：Meta-analysis has been widely used to synthesize evidence from multiple studies for common hypotheses or parameters of interest. However, it has not yet been fully developed for incorporating heterogeneous studies, which arise often in applications due to different study designs, populations, or outcomes. For heterogeneous studies, the parameter of interest may not be estimable for certain studies, and in such a case, these studies are typically excluded from conventional meta-analysis. The ex...
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作者：Zhang, Tingting; Wu, Jingwei; Li, Fan; Caffo, Brian; Boatman-Reich, Dana
作者单位：University of Virginia; Duke University; Johns Hopkins University; Johns Hopkins University
摘要：We introduce a dynamic directional model (DDM) for studying brain effective connectivity based on intracranial electrocorticographic (ECoG) time series. The DDM consists of two parts: a set of differential equations describing neuronal activity of brain components (state equations), and observation equations linking the underlying neuronal states to observed data. When applied to functional MRI or EEG data, DDMs usually have complex formulations and thus can accommodate only a few regions, due...
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作者：Calvet, Laurent E.; Czellar, Veronika; Ronchetti, Elvezio
作者单位：Hautes Etudes Commerciales (HEC) Paris; emlyon business school; University of Geneva; University of Geneva
摘要：Filtering methods are powerful tools to estimate the hidden state of a state-space model from observations available in real time. However, they are known to be highly sensitive to the presence of small misspecifications of the underlying model and to outliers in the observation process. In this article, we show that the methodology of robust statistics can be adapted to sequential filtering. We define a filter as being robust if the relative error in the state distribution caused by misspecif...
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作者：Datta, Gauri Sankar; Mandal, Abhyuday
作者单位：University System of Georgia; University of Georgia
摘要：Random effects models play an important role in model-based small area estimation. Random effects account for any lack of fit of a regression model for the population means of small areas on a set of explanatory variables. In a recent article, Datta, Hall, and Mandal showed that if the random effects can be dispensed with via a suitable test, then the model parameters and the small area means may be estimated with substantially higher accuracy. The work of Datta, Hall, and Mandal is most usefu...
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作者：Guhaniyogi, Rajarshi; Dunson, David B.
作者单位：University of California System; University of California Santa Cruz; Duke University
摘要：As an alternative to variable selection or shrinkage in high-dimensional regression, we propose to randomly compress the predictors prior to analysis. This dramatically reduces storage and computational bottlenecks, performing well when the predictors can be projected to a low-dimensional linear subspace with minimal loss of information about the response. As opposed to existing Bayesian dimensionality reduction approaches, the exact posterior distribution conditional on the compressed data is...
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作者：Lemieux, Thomas
作者单位：University of British Columbia
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作者：McCormick, Tyler H.; Zheng, Tian
作者单位：University of Washington; University of Washington Seattle; Columbia University
摘要：Despite increased interest across a range of scientific applications in modeling and understanding social network structure, collecting complete network data remains logistically and financially challenging, especially in the social sciences. This article introduces a latent surface representation of social network structure for partially observed network data. We derive a multivariate measure of expected (latent) distance between an observed actor and unobserved actors with given features. We...
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作者：Zhao, Jiwei; Shao, Jun
作者单位：State University of New York (SUNY) System; University at Buffalo, SUNY; East China Normal University; University of Wisconsin System; University of Wisconsin Madison
摘要：We consider identifiability and estimation in a generalized linear model in which the response variable and some covariates have missing values and the missing data mechanism is nonignorable and unspecified. We adopt a pseudo-likelihood approach that makes use of an instrumental variable to help identifying unknown parameters in the presence of nonignorable missing data. Explicit conditions for the identifiability of parameters are given. Some asymptotic properties of the parameter estimators ...
