-
作者：Sun, Wei; Liu, Yufeng; Crowley, James J.; Chen, Ting-Huei; Zhou, Hua; Chu, Haitao; Huang, Shunping; Kuan, Pei-Fen; Li, Yuan; Miller, Darla; Shaw, Ginger; Wu, Yichao; Zhabotynsky, Vasyl; McMillan, Leonard; Zou, Fei; Sullivan, Patrick F.; de Villena, Fernando Pardo-Manuel
作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; North Carolina State University; University of Minnesota System; University of Minnesota Twin Cities; University of North Carolina; University of North Carolina Chapel Hill
摘要：We have developed a statistical method named IsoDOT to assess differential isoform expression (DIE) and differential isoform usage (DIU) using RNA-seq data. Here isoform usage refers to relative isoform expression given the total expression of the corresponding gene. IsoDOT performs two tasks that cannot be accomplished by existing methods: to test DIE/DIU with respect to a continuous covariate, and to test DIE/DIU for one case versus one control. The latter task is not an uncommon situation i...
-
作者：Hahn, P. Richard; Carvalho, Carlos M.
作者单位：University of Chicago; University of Texas System; University of Texas Austin
摘要：Selecting a subset of variables for linear models remains an active area of research. This article reviews many of the recent contributions to the Bayesian model selection and shrinkage prior literature. A posterior variable selection summary is proposed, which distills a full posterior distribution over regression coefficients into a sequence of sparse linear predictors.
-
作者：Tang, Xu; Gan, Fah F.; Zhang, Lingyun
作者单位：National University of Singapore; Beijing Normal-Hong Kong Baptist University
摘要：The cumulative sum charting procedure is traditionally used in the manufacturing industry for monitoring the quality of products. Recently, it has been extended to monitoring surgical outcomes. Unlike a manufacturing process where the raw material is usually reasonably homogeneous, patients' risks of surgical failure are usually different. It has been proposed in the literature that the binary outcomes from a surgical procedure be adjusted using the preoperative risk based on a likelihood-rati...
-
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...
-
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...
-
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...
-
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...
-
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...
-
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...
-
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...
