527期

最新文章

• Skinny Gibbs: A Consistent and Scalable Gibbs Sampler for Model Selection AI翻译
  作者：Narisetty, Naveen N.; Shen, Juan; He, Xuming
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; Fudan University; University of Michigan System; University of Michigan
  摘要：We consider the computational and statistical issues for high-dimensional Bayesian model selection under the Gaussian spike and slab priors. To avoid large matrix computations needed in a standard Gibbs sampler, we propose a novel Gibbs sampler called Skinny Gibbs which is much more scalable to high-dimensional problems, both in memory and in computational efficiency. In particular, its computational complexity grows only linearly in p, the number of predictors, while retaining the property of...
• Inferring Brain Signals Synchronicity From a Sample of EEG Readings AI翻译
  作者：Li, Qian; Senturk, Damla; Sugar, Catherine A.; Jeste, Shafali; DiStefano, Charlotte; Frohlich, Joel; Telesca, Donatello
  作者单位：University of California System; University of California Los Angeles; University of California System; University of California Los Angeles; University of California System; University of California Los Angeles
  摘要：Inferring patterns of synchronous brain activity from a heterogeneous sample of electroencephalograms is scientifically and methodologically challenging. While it is intuitively and statistically appealing to rely on readings from more than one individual in order to highlight recurrent patterns of brain activation, pooling information across subjects presents nontrivial methodological problems. We discuss some of the scientific issues associated with the understanding of synchronized neuronal...
• A Bayesian Hierarchical Summary Receiver Operating Characteristic Model for Network Meta-Analysis of Diagnostic Tests AI翻译
  作者：Lian, Qinshu; Hodges, James S.; Chu, Haitao
  作者单位：University of Minnesota System; University of Minnesota Twin Cities
  摘要：In studies evaluating the accuracy of diagnostic tests, three designs are commonly used, crossover, randomized, and noncomparative. Existing methods for meta-analysis of diagnostic tests mainly consider the simple cases in which the reference test in all or none of the studies can be considered a gold standard test, and in which all studies use either a randomized or noncomparative design. The proliferation of diagnostic instruments and the diversity of study designs create a need for more gen...
• Covariate-Adjusted Tensor Classification in High Dimensions AI翻译
  作者：Pan, Yuqing; Mai, Qing; Zhang, Xin
  作者单位：State University System of Florida; Florida State University
  摘要：In contemporary scientific research, it is often of great interest to predict a categorical response based on a high-dimensional tensor (i.e., multi-dimensional array) and additional covariates. Motivated by applications in science and engineering, we propose a comprehensive and interpretable discriminant analysis model, called the CATCH model (short for covariate-adjusted tensor classification in high-dimensions). The CATCH model efficiently integrates the covariates and the tensor to predict...
• Invariant Causal Prediction for Sequential Data AI翻译
  作者：Pfister, Niklas; Buehlmann, Peter; Peters, Jonas
  作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Copenhagen
  摘要：We investigate the problem of inferring the causal predictors of a response Y from a set of d explanatory variables (X-1, ..., X-d). Classical ordinary least-square regression includes all predictors that reduce the variance of Y. Using only the causal predictors instead leads to models that have the advantage of remaining invariant under interventions; loosely speaking they lead to invariance across different environments or heterogeneity patterns. More precisely, the conditional distribution...
• Calibration Concordance for Astronomical Instruments via Multiplicative Shrinkage AI翻译
  作者：Chen, Yang; Meng, Xiao-Li; Wang, Xufei; van Dyk, David A.; Marshall, Herman L.; Kashyap, Vinay L.
  作者单位：University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; Harvard University; Imperial College London; Massachusetts Institute of Technology (MIT); Harvard University; Smithsonian Astrophysical Observatory; Smithsonian Institution
  摘要：Calibration data are often obtained by observing several well-understood objects simultaneously with multiple instruments, such as satellites for measuring astronomical sources. Analyzing such data and obtaining proper concordance among the instruments is challenging when the physical source models are not well understood, when there are uncertainties in known physical quantities, or when data quality varies in ways that cannot be fully quantified. Furthermore, the number of model parameters i...
• Robust Bayesian Inference via Coarsening AI翻译
  作者：Miller, Jeffrey W.; Dunson, David B.
  作者单位：Harvard University; Duke University
  摘要：The standard approach to Bayesian inference is based on the assumption that the distribution of the data belongs to the chosen model class. However, even a small violation of this assumption can have a large impact on the outcome of a Bayesian procedure. We introduce a novel approach to Bayesian inference that improves robustness to small departures from the model: rather than conditioning on the event that the observed data are generated by the model, one conditions on the event that the mode...
• Multiple Testing When Many p-Values are Uniformly Conservative, with Application to Testing Qualitative Interaction in Educational Interventions AI翻译
  作者：Zhao, Qingyuan; Small, Dylan S.; Su, Weijie
  作者单位：University of Pennsylvania
  摘要：In the evaluation of treatment effects, it is of major policy interest to know if the treatment is beneficial for some and harmful for others, a phenomenon known as qualitative interaction. We formulate this question as a multiple testing problem with many conservative null p-values, in which the classical multiple testing methods may lose power substantially. We propose a simple technique-conditioning-to improve the power. A crucial assumption we need is uniform conservativeness, meaning for ...
• Combined Hypothesis Testing on Graphs With Applications to Gene Set Enrichment Analysis AI翻译
  作者：Wang, Shulei; Yuan, Ming
  作者单位：University of Wisconsin System; University of Wisconsin Madison; Columbia University
  摘要：Motivated by gene set enrichment analysis, we investigate the problem of combined hypothesis testing on a graph. A general framework is introduced to make effective use of the structural information of the underlying graph when testing multivariate means. A new testing procedure is proposed within this framework, and shown to be optimal in that it can consistently detect departures from the collective null at a rate that no other test could improve, for almost all graphs. We also provide gener...
• Proper Inference for Value Function in High-Dimensional Q-Learning for Dynamic Treatment Regimes AI翻译
  作者：Zhu, Wensheng; Zeng, Donglin; Song, Rui
  作者单位：Northeast Normal University - China; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; North Carolina State University
  摘要：Dynamic treatment regimes are a set of decision rules and each treatment decision is tailored over time according to patients' responses to previous treatments as well as covariate history. There is a growing interest in development of correct statistical inference for optimal dynamic treatment regimes to handle the challenges of nonregularity problems in the presence of nonrespondents who have zero-treatment effects, especially when the dimension of the tailoring variables is high. In this ar...