Journal of the American Statistical Association

最新文章

• Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confounding AI翻译
  作者：Dorn, Jacob; Guo, Kevin; Kallus, Nathan
  作者单位：Princeton University; Stanford University; Cornell University
  摘要：We consider the problem of constructing bounds on the average treatment effect (ATE) when unmeasured confounders exist but have bounded influence. Specifically, we assume that omitted confounders could not change the odds of treatment for any unit by more than a fixed factor. We derive the sharp partial identification bounds implied by this assumption by leveraging distributionally robust optimization, and we propose estimators of these bounds with several novel robustness properties. The firs...
• An Interpretable and Efficient Infinite-Order Vector Autoregressive Model for High-Dimensional Time Series AI翻译
  作者：Zheng, Yao
  作者单位：University of Connecticut
  摘要：As a special infinite-order vector autoregressive (VAR) model, the vector autoregressive moving average (VARMA) model can capture much richer temporal patterns than the widely used finite-order VAR model. However, its practicality has long been hindered by its non-identifiability, computational intractability, and difficulty of interpretation, especially for high-dimensional time series. This article proposes a novel sparse infinite-order VAR model for high-dimensional time series, which avoid...
• Poisson-FOCuS: An Efficient Online Method for Detecting Count Bursts with Application to Gamma Ray Burst Detection AI翻译
  作者：Ward, Kes; Dilillo, Giuseppe; Eckley, Idris; Fearnhead, Paul
  作者单位：Lancaster University; Istituto Nazionale Astrofisica (INAF); University of Udine; Lancaster University
  摘要：Gamma ray bursts are flashes of light from distant, new-born black holes. CubeSats that monitor high-energy photons across different energy bands are used to detect these bursts. There is a need for computationally efficient algorithms, able to run using the limited computational resource onboard a CubeSats, that can detect when gamma ray bursts occur. Current algorithms are based on monitoring photon counts across a grid of different sizes of time window. We propose a new method, which extend...
• 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 Medical Center (LUMC); Leiden University - Excl 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