Journal of the American Statistical Association

最新文章

• Inference for High-Dimensional Censored Quantile Regression AI翻译
  作者：Fei, Zhe; Zheng, Qi; Hong, Hyokyoung G.; Li, Yi
  作者单位：University of California System; University of California Los Angeles; University of Louisville; Michigan State University; University of Michigan System; University of Michigan
  摘要：With the availability of high-dimensional genetic biomarkers, it is of interest to identify heterogeneous effects of these predictors on patients' survival, along with proper statistical inference. Censored quantile regression has emerged as a powerful tool for detecting heterogeneous effects of covariates on survival outcomes. To our knowledge, there is little work available to draw inferences on the effects of high-dimensional predictors for censored quantile regression (CQR). This article p...
• Treatment Effect Estimation Under Additive Hazards Models With High-Dimensional Confounding AI翻译
  作者：Hou, Jue; Bradic, Jelena; Xu, Ronghui
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; University of California System; University of California San Diego; University of California System; University of California San Diego; University of California System; University of California San Diego
  摘要：Estimating treatment effects for survival outcomes in the high-dimensional setting is critical for many biomedical applications and any application with censored observations. This article establishes an orthogonal score for learning treatment effects, using observational data with a potentially large number of confounders. The estimator allows for root-n, asymptotically valid confidence intervals, despite the bias induced by the regularization. Moreover, we develop a novel hazard difference (...
• 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
• Covariate-Informed Latent Interaction Models: Addressing Geographic & Taxonomic Bias in Predicting Bird-Plant Interactions AI翻译
  作者：Papadogeorgou, Georgia; Bello, Carolina; Ovaskainen, Otso; Dunson, David B.
  作者单位：State University System of Florida; University of Florida; Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Jyvaskyla; University of Helsinki; Norwegian University of Science & Technology (NTNU); Duke University
  摘要：Reductions in natural habitats urge that we better understand species' interconnection and how biological communities respond to environmental changes. However, ecological studies of species' interactions are limited by their geographic and taxonomic focus which can distort our understanding of interaction dynamics. We focus on bird-plant interactions that refer to situations of potential fruit consumption and seed dispersal. We develop an approach for predicting species' interactions that acc...
• 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...
• Bayesian Inference Using Synthetic Likelihood: Asymptotics and Adjustments AI翻译
  作者：Frazier, David T.; Nott, David J.; Drovandi, Christopher; Kohn, Robert
  作者单位：Monash University; National University of Singapore; National University of Singapore; University of Queensland; University of New South Wales Sydney
  摘要：Implementing Bayesian inference is often computationally challenging in complex models, especially when calculating the likelihood is difficult. Synthetic likelihood is one approach for carrying out inference when the likelihood is intractable, but it is straightforward to simulate from the model. The method constructs an approximate likelihood by taking a vector summary statistic as being multivariate normal, with the unknown mean and covariance estimated by simulation. Previous research demo...
• 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...
• A General Framework for Inference on Algorithm-Agnostic Variable Importance AI翻译
  作者：Williamson, Brian D.; Gilbert, Peter B.; Simon, Noah R.; Carone, Marco
  作者单位：Fred Hutchinson Cancer Center; University of Washington; University of Washington Seattle
  摘要：In many applications, it is of interest to assess the relative contribution of features (or subsets of features) toward the goal of predicting a response-in other words, to gauge the variable importance of features. Most recent work on variable importance assessment has focused on describing the importance of features within the confines of a given prediction algorithm. However, such assessment does not necessarily characterize the prediction potential of features, and may provide a misleading...