Journal of the American Statistical Association

最新文章

• Scan Statistics for the Detection of Anomalies in M-Dependent Random Fields with Applications to Image Data AI翻译
  作者：Kirch, Claudia; Klein, Philipp; Meyer, Marco
  作者单位：Otto von Guericke University; Leibniz University Hannover
  摘要：Anomaly detection in random fields is an important problem in many applications including the detection of cancerous cells in medicine, obstacles in autonomous driving and cracks in the construction material of buildings. Such anomalies are often visible as areas with different expected values compared to the background noise. Scan statistics based on local means have the potential to detect such local anomalies by enhancing relevant features. We derive limit theorems for a general class of su...
• A Regression Framework for Studying Relationships among Attributes under Network Interference AI翻译
  作者：Fritz, Cornelius; Schweinberger, Michael; Bhadra, Subhankar; Hunter, David R.
  作者单位：Trinity College Dublin; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：To understand how the interconnected and interdependent world of the twenty-first century operates and make model-based predictions, joint probability models for networks and interdependent outcomes are needed. We propose a comprehensive regression framework for networks and interdependent outcomes with multiple advantages, including interpretability, scalability, and provable theoretical guarantees. The regression framework can be used for studying relationships among attributes of connected ...
• Bayesian Geostatistics Using Predictive Stacking AI翻译
  作者：Zhang, Lu; Tang, Wenpin; Banerjee, Sudipto
  作者单位：University of Southern California; Columbia University; University of California System; University of California Los Angeles
  摘要：We develop Bayesian predictive stacking for geostatistical models, where the primary inferential objective is to provide inference on the latent spatial random field and conduct spatial predictions at arbitrary locations. We exploit analytically tractable posterior distributions for regression coefficients of predictors and the realizations of the spatial process conditional upon process parameters. We subsequently combine such inference by stacking these models across the range of values of t...
• Communication-Efficient Distributed Estimation and Inference for Cox's Model AI翻译
  作者：Bayle, Pierre; Fan, Jianqing; Lou, Zhipeng
  作者单位：Princeton University; University of California System; University of California San Diego
  摘要：Motivated by multi-center biomedical studies that cannot share individual data due to privacy and ownership concerns, we develop communication-efficient iterative distributed algorithms for estimation and inference in the high-dimensional sparse Cox proportional hazards model. We demonstrate that our estimator, even with a relatively small number of iterations, achieves the same convergence rate as the ideal full-sample estimator under very mild conditions. To construct confidence intervals fo...
• Adjacency Matrix Decomposition Clustering for Human Activity Data AI翻译
  作者：Barnard, Martha; Fan, Yingling; Wolfson, Julian
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; University of Minnesota System; University of Minnesota Twin Cities
  摘要：Mobile apps and wearable devices accurately and continuously measure human activity; patterns within this data can provide a wealth of information applicable to fields such as transportation and health. Despite the potential utility of this data, there has been limited development of analysis methods for sequences of daily activities. In this article, we propose a novel clustering method and cluster evaluation metric for human activity data that leverages an adjacency matrix representation to ...
• Inference in Generalized Linear Models with Robustness to Misspecified Variances AI翻译
  作者：De Santis, Riccardo; Goeman, Jelle J.; Hemerik, Jesse; Davenport, Samuel; Finos, Livio
  作者单位：University of Padua; Leiden University; Leiden University - Excl LUMC; Leiden University Medical Center (LUMC); Erasmus University Rotterdam; Erasmus University Rotterdam - Excl Erasmus MC; University of California System; University of California San Diego
  摘要：Generalized linear models usually assume a common dispersion parameter, an assumption that is seldom true in practice. Consequently, standard parametric methods may suffer appreciable loss of Type I error control. As an alternative, we present a semi-parametric group-invariance method based on sign flipping of score contributions. Our method requires only the correct specification of the mean model, but is robust against any misspecification of the variance. We present tests for single as well...
• Hypothesis Testing for a Functional Parameter via Self-Normalization AI翻译
  作者：Zhang, Yi; Shao, Xiaofeng
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; Washington University (WUSTL); Washington University (WUSTL)
  摘要：Testing simple or composite hypothesis on a functional parameter has attracted considerable attention in time series analysis. To accommodate for the unknown temporal dependence, classical nonparametric approaches such as block bootstrapping and subsampling all involve a bandwidth parameter, the choice of which can substantially affect the finite sample performance. The self normalization (SN) method is tuning parameter free when applied to the inference of a finite-dimensional parameter but i...
• Handbook of Bayesian, Fiducial, and Frequentist Inference AI翻译
  作者：Gu, Mengyang
  作者单位：University of California System; University of California Santa Barbara
• Objective Bayesian Inference AI翻译
  作者：Lee, Jaeyong
  作者单位：Seoul National University (SNU)
• ROC Analysis for Classification and Prediction in Practice AI翻译
  作者：Tec, Mauricio
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health