543期

最新文章

• Threshold Selection in Feature Screening for Error Rate Control AI翻译
  作者：Guo, Xu; Ren, Haojie; Zou, Changliang; Li, Runze
  作者单位：Beijing Normal University; Shanghai Jiao Tong University; Nankai University; Nankai University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：Hard thresholding rule is commonly adopted in feature screening procedures to screen out unimportant predictors for ultrahigh-dimensional data. However, different thresholds are required to adapt to different contexts of screening problems and an appropriate thresholding magnitude usually varies from the model and error distribution. With an ad-hoc choice, it is unclear whether all of the important predictors are selected or not, and it is very likely that the procedures would include many uni...
• Bootstrap Prediction Bands for Functional Time Series (vol 118, pg 972, 2021) AI翻译
  作者：Paparoditis, Efstathios; Shang, Han Lin
  作者单位：University of Cyprus; Macquarie University
• Identifying Effects of Multiple Treatments in the Presence of Unmeasured Confounding AI翻译
  作者：Miao, Wang; Hu, Wenjie; Ogburn, Elizabeth L.; Zhou, Xiao-Hua
  作者单位：Peking University; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Peking University; Peking University
  摘要：Identification of treatment effects in the presence of unmeasured confounding is a persistent problem in the social, biological, and medical sciences. The problem of unmeasured confounding in settings with multiple treatments is most common in statistical genetics and bioinformatics settings, where researchers have developed many successful statistical strategies without engaging deeply with the causal aspects of the problem. Recently there have been a number of attempts to bridge the gap betw...
• Discussion of A Scale-Free Approach for False Discovery Rate Control in Generalized Linear Models AI翻译
  作者：Law, Michael; Buehlmann, Peter
  作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich
• A Wavelet-Based Independence Test for Functional Data With an Application to MEG Functional Connectivity AI翻译
  作者：Miao, Rui; Zhang, Xiaoke; Wong, Raymond K. W.
  作者单位：George Washington University; Texas A&M University System; Texas A&M University College Station
  摘要：Measuring and testing the dependency between multiple random functions is often an important task in functional data analysis. In the literature, a model-based method relies on a model which is subject to the risk of model misspecification, while a model-free method only provides a correlation measure which is inadequate to test independence. In this paper, we adopt the Hilbert-Schmidt Independence Criterion (HSIC) to measure the dependency between two random functions. We develop a two-step p...
• Modern Applied Regressions: Bayesian and Frequentist Analysis of Categorical and Limited Response variables with R and Stan AI翻译
  作者：Harris, Mark N.
  作者单位：Curtin University
• Compositional Graphical Lasso Resolves the Impact of Parasitic Infection on Gut Microbial Interaction Networks in a Zebrafish Model AI翻译
  作者：Tian, Chuan; Jiang, Duo; Hammer, Austin; Sharpton, Thomas; Jiang, Yuan
  作者单位：Oregon State University; Oregon State University
  摘要：Understanding how microbes interact with each other is key to revealing the underlying role that microorganisms play in the host or environment and to identifying microorganisms as an agent that can potentially alter the host or environment. For example, understanding how the microbial interactions associate with parasitic infection can help resolve potential drug or diagnostic test for parasitic infection. To unravel the microbial interactions, existing tools often rely on graphical models to...
• Genetic Underpinnings of Brain Structural Connectome for Young Adults AI翻译
  作者：Zhao, Yize; Chang, Changgee; Zhang, Jingwen; Zhang, Zhengwu
  作者单位：Yale University; University of Pennsylvania; Boston University; University of North Carolina; University of North Carolina Chapel Hill
  摘要：With distinct advantages in power over behavioral phenotypes, brain imaging traits have become emerging endophenotypes to dissect molecular contributions to behaviors and neuropsychiatric illnesses. Among different imaging features, brain structural connectivity (i.e., structural connectome) which summarizes the anatomical connections between different brain regions is one of the most cutting edge while under-investigated traits; and the genetic influence on the structural connectome variation...
• Adaptive Conditional Distribution Estimation with Bayesian Decision Tree Ensembles AI翻译
  作者：Li, Yinpu; Linero, Antonio R.; Murray, Jared
  作者单位：State University System of Florida; Florida State University; University of Texas System; University of Texas Austin
  摘要：We present a Bayesian nonparametric model for conditional distribution estimation using Bayesian additive regression trees (BART). The generative model we use is based on rejection sampling from a base model. Like other BART models, our model is flexible, has a default prior specification, and is computationally convenient. To address the distinguished role of the response in our BART model, we introduce an approach to targeted smoothing of BART models which is of independent interest. We stud...
• Query-Augmented Active Metric Learning AI翻译
  作者：Deng, Yujia; Yuan, Yubai; Fu, Haoda; Qu, Annie
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of California System; University of California Irvine; Eli Lilly
  摘要：In this article, we propose an active metric learning method for clustering with pairwise constraints. The proposed method actively queries the label of informative instance pairs, while estimating underlying metrics by incorporating unlabeled instance pairs, which leads to a more accurate and efficient clustering process. In particular, we augment the queried constraints by generating more pairwise labels to provide additional information in learning a metric to enhance clustering performance...