517期

最新文章

• Fast Approximate Inference for Arbitrarily Large Semiparametric Regression Models via Message Passing Comment AI翻译
  作者：Reiss, Philip T.; Goldsmith, Jeff
  作者单位：University of Haifa; New York University; Columbia University
• Geometric Representations of Random Hypergraphs AI翻译
  作者：Lunagomez, Simon; Mukherjee, Sayan; Wolpert, Robert L.; Airoldi, Edoardo M.
  作者单位：Harvard University; Duke University
  摘要：We introduce a novel parameterization of distributions on hypergraphs based on the geometry of points in . The idea is to induce distributions on hypergraphs by placing priors on point configurations via spatial processes. This specification is then used to infer conditional independence models, or Markov structure, for multivariate distributions. This approach results in a broader class of conditional independence models beyond standard graphical models. Factorizations that cannot be retrieve...
• A Bayesian Race Model for Recognition Memory AI翻译
  作者：Kim, Sungmin; Potter, Kevin; Craigmile, Peter F.; Peruggia, Mario; Van Zandt, Trisha
  作者单位：University System of Ohio; Ohio State University; University System of Ohio; Ohio State University
  摘要：Many psychological models use the idea of a trace, which represents a change in a person's cognitive state that arises as a result of processing a given stimulus. These models assume that a trace is always laid down when a stimulus is processed. In addition, some of these models explain how response times (RTs) and response accuracies arise from a process in which the different traces race against each other.In this article, we present a Bayesian hierarchical model of RT and accuracy in a diff...
• Partition MCMC for Inference on Acyclic Digraphs AI翻译
  作者：Kuipers, Jack; Moffa, Giusi
  作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich; University of London; University College London
  摘要：Acyclic digraphs are the underlying representation of Bayesian networks, a widely used class of probabilistic graphical models. Learning the underlying graph from data is a way of gaining insights about the structural properties of a domain. Structure learning forms one of the inference challenges of statistical graphical models. Markov chain Monte Carlo (MCMC) methods, notably structure MCMC, to sample graphs from the posterior distribution given the data are probably the only viable option f...
• Weighted Statistic in Detecting Faint and Sparse Alternatives for High-Dimensional Covariance Matrices AI翻译
  作者：Yang, Qing; Pan, Guangming
  作者单位：Nanyang Technological University
  摘要：This article considers testing equality of two population covariance matrices when the data dimension p diverges with the sample size n (p/n c > 0). We propose a weighted test statistic that is data-driven and powerful in both faint alternatives (many small disturbances) and sparse alternatives (several large disturbances). Its asymptotic null distribution is derived by large random matrix theory without assuming the existence of a limiting cumulative distribution function of the population co...
• Covariance Regression Analysis AI翻译
  作者：Zou, Tao; Lan, Wei; Wang, Hansheng; Tsai, Chih-Ling
  作者单位：Peking University; Australian National University; Southwestern University of Finance & Economics - China; Southwestern University of Finance & Economics - China; University of California System; University of California Davis
  摘要：This article introduces covariance regression analysis for a p-dimensional response vector. The proposed method explores the regression relationship between the p-dimensional covariance matrix and auxiliary information. We study three types of estimators: maximum likelihood, ordinary least squares, and feasible generalized least squares estimators. Then, we demonstrate that these regression estimators are consistent and asymptotically normal. Furthermore, we obtain the high dimensional and lar...
• Randomization Inference and Sensitivity Analysis for Composite Null Hypotheses With Binary Outcomes in Matched Observational Studies AI翻译
  作者：Fogarty, Colin B.; Shi, Pixu; Mikkelsen, Mark E.; Small, Dylan S.
  作者单位：Massachusetts Institute of Technology (MIT); University of Pennsylvania; University of Pennsylvania; University of Pennsylvania
  摘要：We present methods for conducting hypothesis testing and sensitivity analyses for composite null hypotheses in matched observational studies when outcomes are binary. Causal estimands discussed include the causal risk difference, causal risk ratio, and the effect ratio. We show that inference under the assumption of no unmeasured confounding can be performed by solving an integer linear program, while inference allowing for unmeasured confounding of a given strength requires solving an integer...
• A Concave Pairwise Fusion Approach to Subgroup Analysis AI翻译
  作者：Ma, Shujie; Huang, Jian
  作者单位：University of California System; University of California Riverside; University of Iowa
  摘要：An important step in developing individualized treatment strategies is correct identification of subgroups of a heterogeneous population to allow specific treatment for each subgroup. This article considers the problem using samples drawn from a population consisting of subgroups with different mean values, along with certain covariates. We propose a penalized approach for subgroup analysis based on a regression model, in which heterogeneity is driven by unobserved latent factors and thus can ...