Journal of the American Statistical Association

最新文章

• Generalized Linear Mixed Models: Modern Concepts, Methods and Applications, 2nd ed. AI翻译
  作者：Liu, Xing
  作者单位：Connecticut State University System; Eastern Connecticut State University
• Global and Episode-Specific Prediction of Recurrent Events Using Longitudinal Health Informatics Data AI翻译
  作者：Sun, Yifei; Chiou, Sy Han; Huang, Chiung-Yu
  作者单位：Columbia University; Southern Methodist University; University of California System; University of California San Francisco
  摘要：Accurate prediction of recurrent clinical events is crucial for effective management of chronic conditions such as cancer and cardiovascular disease. In recent years, longitudinal health informatics databases, which routinely collect data on repeated clinical events, have been increasingly used to construct risk prediction models. We introduce a novel nonparametric framework to predict recurrent events on a gap time scale using survival tree ensembles. Our framework incorporates two predictive...
• Statistical Inference for High-Dimensional Spectral Density Matrix AI翻译
  作者：Chang, Jinyuan; Jiang, Qing; Mcelroy, Tucker; Shao, Xiaofeng
  作者单位：Southwestern University of Finance & Economics - China; Southwestern University of Finance & Economics - China; Chinese Academy of Sciences; Nanjing Institute of Geology & Paleontology, CAS; Academy of Mathematics & System Sciences, CAS; Beijing Normal University; Washington University (WUSTL); Washington University (WUSTL)
  摘要：The spectral density matrix is a fundamental object of interest in time series analysis, and it encodes both contemporary and dynamic linear relationships between component processes of the multivariate system. In this article we develop novel inference procedures for the spectral density matrix in the high-dimensional setting. Specifically, we introduce a new global testing procedure to test the nullity of the cross-spectral density for a given set of frequencies and across pairs of component...
• Testing Mutually Exclusive Hypotheses for Multi-Response Regressions AI翻译
  作者：Huang, Jiaqi; Zhao, Wenbiao; Zhu, Lixing
  作者单位：Beijing Normal University; China University of Mining & Technology; Beijing Normal University; Beijing Normal University Zhuhai
  摘要：This article proposes an adaptive-to-model test to check the null hypothesis with no more than one coordinate of the response vector relating to the predictor vector in parametric multi-response regressions. To this end, we decompose the null hypothesis into several mutually exclusive sub-null hypotheses and suggest a model identification to construct an adaptive-to-sub-null hypothesis test tackling their mutual exclusiveness, and an adaptive-to-regression test handling the regression function...
• Inferring Causal Effect of a Digital Communication Strategy under a Latent Sequential Ignorability Assumption and Treatment Noncompliance AI翻译
  作者：Ohnishi, Yuki; Karmakar, Bikram; Kar, Wreetabrata
  作者单位：Yale University; State University System of Florida; University of Florida; State University of New York (SUNY) System; University at Albany, SUNY; University at Buffalo, SUNY
  摘要：Organizations are increasingly relying on digital communications, such as targeted e-mails and mobile notifications, to engage with their audiences. Despite the evident advantages like cost-effectiveness and customization, assessing the effectiveness of such communications from observational data poses various statistical challenges. An immediate challenge is to adjust for targeting rules used in these communications. When digital communications involve a sequence of e-mails or notifications, ...
• Discussion of Data Fission: Splitting a Single Data Point - Some Asymptotic Results for Data Fission AI翻译
  作者：Lei, Lihua
  作者单位：Stanford University; Stanford University
• Poisson-FOCuS: An Efficient Online Method for Detecting Count Bursts with Application to Gamma Ray Burst Detection. Rejoinder AI翻译
  作者：Ward, Kes; Dilillo, Giuseppe; Eckley, Idris; Fearnhead, Paul
  作者单位：Lancaster University; Istituto Nazionale Astrofisica (INAF); Lancaster University
• Collective Outlier Detection and Enumeration with Conformalized Closed Testing AI翻译
  作者：Magnani, Chiara G.; Sesia, Matteo; Solari, Aldo
  作者单位：Bocconi University; University of Southern California; University of Southern California; Universita Ca Foscari Venezia; University of Milano-Bicocca
  摘要：This article develops a flexible distribution-free method for collective outlier detection and enumeration, designed for situations in which the presence of outliers can be detected powerfully even though their precise identification may be challenging due to the sparsity, weakness, or elusiveness of their signals. This method builds upon recent developments in conformal inference and integrates classical ideas from other areas, including multiple testing, locally most powerful and adaptive ra...
• Fairness-Aware Gaussian Graphical Regression Models with Application to Brain Co-Expression QTL Studies AI翻译
  作者：Zhou, Xingcai; Xu, Zinan; Jiang, Bei; Kong, Linglong
  作者单位：Nanjing Audit University; University of Alberta
  摘要：Glioblastoma multiforme (GBM) is a highly aggressive brain cancer with largely ineffective treatment. It is imperative to explore more effective therapies, such as gene-based treatments. For co-expression QTL studies of GBM, we develop fairness-aware Gaussian graphical regression models (Fair RegGGMs), which can determine how genetic variants modulate subject-level gene networks, and recover both population-level and subject-level gene graphs, while ensuring that the developed learning and inf...
• Interpretable Scalar-on-Image Linear Regression Models via the Generalized Dantzig Selector AI翻译
  作者：Liao, Sijia; Sun, Xiaoxiao; Hao, Ning; Zhang, Hao Helen
  作者单位：University of Arizona; University of Arizona; University of Arizona
  摘要：The scalar-on-image regression model examines the association between a scalar response and a bivariate function (e.g., images) through the estimation of a bivariate coefficient function. Existing approaches often impose smoothness constraints to control the bias-variance trade-off, and thus prevent overfitting. However, such assumptions can hinder interpretability, especially when only certain regions of an image influence changes in the response. In such a scenario, interpretability can be b...