547期

最新文章

• Bayesian Integrative Region Segmentation in Spatially Resolved Transcriptomic Studies AI翻译
  作者：Yan, Yinqiao; Luo, Xiangyu
  作者单位：Renmin University of China
  摘要：The spatially resolved transcriptomic study is a recently developed biological experiment that can measure gene expressions and retain spatial information simultaneously, opening a new avenue to characterize fine-grained tissue structures. In this article, we propose a nonparametric Bayesian method named BINRES to carry out the region segmentation for a tissue section by integrating all the three types of data generated during the study-gene expressions, spatial coordinates, and the histology ...
• Controlled Epidemiological Studies AI翻译
  作者：Ghosh, Kaushik
  作者单位：Nevada System of Higher Education (NSHE); University of Nevada Las Vegas
• Tree-Guided Rare Feature Selection and Logic Aggregation with Electronic Health Records Data AI翻译
  作者：Chen, Jianmin; Aseltine, Robert H.; Wang, Fei; Chen, Kun
  作者单位：University of Connecticut; Cornell University; Weill Cornell Medicine
  摘要：Statistical learning with a large number of rare binary features is commonly encountered in analyzing electronic health records (EHR) data, especially in the modeling of disease onset with prior medical diagnoses and procedures. Dealing with the resulting highly sparse and large-scale binary feature matrix is notoriously challenging as conventional methods may suffer from a lack of power in testing and inconsistency in model fitting, while machine learning methods may suffer from the inability...
• Graphical Model Inference with Erosely Measured Data AI翻译
  作者：Zheng, Lili; Allen, Genevera I.
  作者单位：Rice University; Rice University; Rice University; Baylor College of Medicine; Baylor College of Medicine; Baylor College Medical Hospital
  摘要：In this article, we investigate the Gaussian graphical model inference problem in a novel setting that we call erose measurements, referring to irregularly measured or observed data. For graphs, this results in different node pairs having vastly different sample sizes which frequently arises in data integration, genomics, neuroscience, and sensor networks. Existing works characterize the graph selection performance using the minimum pairwise sample size, which provides little insights for eros...
• Spectral Clustering, Bayesian Spanning Forest, and Forest Process AI翻译
  作者：Duan, Leo L.; Roy, Arkaprava
  作者单位：State University System of Florida; University of Florida; State University System of Florida; University of Florida
  摘要：Spectral clustering views the similarity matrix as a weighted graph, and partitions the data by minimizing a graph-cut loss. Since it minimizes the across-cluster similarity, there is no need to model the distribution within each cluster. As a result, one reduces the chance of model misspecification, which is often a risk in mixture model-based clustering. Nevertheless, compared to the latter, spectral clustering has no direct ways of quantifying the clustering uncertainty (such as the assignm...
• Statistical Inference for Maximin Effects: Identifying Stable Associations across Multiple Studies AI翻译
  作者：Guo, Zijian
  作者单位：Rutgers University System; Rutgers University New Brunswick
  摘要：Integrative analysis of data from multiple sources is critical to making generalizable discoveries. Associations consistently observed across multiple source populations are more likely to be generalized to target populations with possible distributional shifts. In this article, we model the heterogeneous multi-source data with multiple high-dimensional regressions and make inferences for the maximin effect (Meinshausen and B & uuml;hlmann, AoS, 43(4), 1801-1830). The maximin effect provides a...
• Value Enhancement of Reinforcement Learning via Efficient and Robust Trust Region Optimization AI翻译
  作者：Shi, Chengchun; Qi, Zhengling; Wang, Jianing; Zhou, Fan
  作者单位：University of London; London School Economics & Political Science; George Washington University; Shanghai University of Finance & Economics
  摘要：Reinforcement learning (RL) is a powerful machine learning technique that enables an intelligent agent to learn an optimal policy that maximizes the cumulative rewards in sequential decision making. Most of methods in the existing literature are developed in online settings where the data are easy to collect or simulate. Motivated by high stake domains such as mobile health studies with limited and pre-collected data, in this article, we study offline reinforcement learning methods. To efficie...
• Empirical Likelihood for Network Data AI翻译
  作者：Matsushita, Yukitoshi; Otsu, Taisuke
  作者单位：Hitotsubashi University; University of London; London School Economics & Political Science
  摘要：This article develops a concept of nonparametric likelihood for network data based on network moments, and proposes general inference methods by adapting the theory of jackknife empirical likelihood. Our methodology can be used not only to conduct inference on population network moments and parameters in network formation models, but also to implement goodness-of-fit testing, such as testing block size for stochastic block models. Theoretically we show that the jackknife empirical likelihood s...
• Statistical Methods in Health Disparity Research AI翻译
  作者：Paddock, Susan M.
  作者单位：University of Chicago
• De-confounding Causal Inference Using Latent Multiple-Mediator Pathways AI翻译
  作者：Yuan, Yubai; Qu, Annie
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of California System; University of California Irvine
  摘要：Causal effect estimation from observational data is one of the essential problems in causal inference. However, most estimation methods rely on the strong assumption that all confounders are observed, which is impractical and untestable in the real world. We develop a mediation analysis framework inferring the latent confounder for debiasing both direct and indirect causal effects. Specifically, we introduce generalized structural equation modeling that incorporates structured latent factors t...