Journal of the American Statistical Association

最新文章

• Posterior Predictive Design for Phase I Clinical Trials AI翻译
  作者：Fu, Chenqi; Zhou, Shouhao; Lee, J. Jack
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Penn State Health; University of Texas System; UTMD Anderson Cancer Center
  摘要：Interval-based designs represent cutting-edge adaptive methodologies for phase I clinical trials to identify the maximum tolerated dose (MTD). These designs exhibit robust performance comparable to more intricate, model-based designs, and their pretabulated decision rule enables them to be implemented as simply as the conventional algorithm-based designs. In this paper, we introduce the posterior predictive (PoP) design, a novel interval-based design that leverages advanced Bayesian predictive...
• Positive and Unlabeled Data: Model, Estimation, Inference, and Classification AI翻译
  作者：Liu, Siyan; Yeh, Chi-Kuang; Zhang, Xin; Tian, Qinglong; Li, Pengfei
  作者单位：East China Normal University; University of Waterloo
  摘要：This study introduces a new approach to addressing the positive and unlabeled (PU) data through the double exponential tilting model (DETM) under a transfer learning framework. Traditional methods often fall short because they only apply to the common distributions (CD) PU data (also known as the selected completely at random PU data), where the labeled positive and unlabeled positive data are assumed to be from the same distribution. In contrast, our DETM's dual structure effectively accommod...
• Discussion on Poisson-FOCuS: An Efficient Online Method for Detecting Count Bursts with Application to Gamma Ray Burst Detection by Ward, Dilillo, Eckley and Fearnhead AI翻译
  作者：Chen, Yang
  作者单位：University of Michigan System; University of Michigan
• The Rejoinder AI翻译
  作者：Leiner, James; Duan, Boyan; Wasserman, Larry; Ramdas, Aaditya
  作者单位：Carnegie Mellon University; Carnegie Mellon University; Alphabet Inc.; Google Incorporated
• Optimizing Sequential Decision Rules for Prostate Cancer Biopsy Management: A Multi-Objective Statistical Framework AI翻译
  作者：Qiu, Jiaming; Zhao, Ying-Qi; Wei, John; Chinnaiyan, Arul M.; Tosoian, Jeffrey; Zheng, Yingye
  作者单位：Fred Hutchinson Cancer Center; University of Michigan System; University of Michigan; Vanderbilt University
  摘要：Binary medical decision-making increasingly demands sequential diagnostic strategies that optimize accuracy while minimizing patient burden and healthcare costs. In prostate cancer diagnosis, many patients undergo unnecessary biopsies despite existing biomarkers and imaging tests that already inform risk stratification. Sequential testing, where tests are selectively administered based on prior results, offers a promising approach to balance diagnostic power with procedural efficiency. We prop...
• Robust Sliced Inverse Regression: Optimal Estimation for Heavy-Tailed Data in High Dimensions AI翻译
  作者：Zeng, Jing; Min, Keqian; Mai, Qing
  作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; State University System of Florida; Florida State University
  摘要：Sliced inverse regression (SIR) is a flexible modeling tool that effectively reduces dimensions to reveal the complicated mechanism behind data. In recent years, SIR has been generalized to high dimensions in a variety of ways. However, all existing methods rely on the light-tailed assumption for predictors, which is frequently violated in real life. To tackle ubiquitous heavy-tailed data, we propose a novel robust SIR method, referred to as ROSE, that scales well with high dimensions and heav...
• Inference for Deep Neural Network Estimators in Generalized Nonparametric Models AI翻译
  作者：Meng, Xuran; Li, Yi
  作者单位：University of Michigan System; University of Michigan
  摘要：While deep neural networks (DNNs) are used for prediction, inference on DNN-estimated subject-specific means for categorical or exponential family outcomes remains underexplored. We address this by proposing a DNN estimator under generalized nonparametric regression models (GNRMs) and developing a rigorous inference framework. Unlike existing approaches that assume independence between estimation errors and inputs to establish the error bound, a condition often violated in GNRMs, we allow for ...
• Tail Risk in the Tail: Estimating High Quantiles When a Related Variable is Extreme AI翻译
  作者：Nolde, Natalia; Zhou, Chen; Zhou, Menglin
  作者单位：University of British Columbia; Erasmus University Rotterdam - Excl Erasmus MC; Erasmus University Rotterdam
  摘要：In this article we address the problem of high quantile estimation conditional on a related variable being extreme. The problem set-up is of interest in a number applications to evaluate tail risk of a focal variable in the tail of a conditioning variable. A primary example we consider is the assessment of systemic risk in financial markets using a risk measure known as the conditional value-at-risk (CoVaR). The proposed estimator is based on a novel approach to handle the bivariate tail depen...
• Out-of-Distribution Generalization under Random, Dense Distributional Shifts AI翻译
  作者：Jeong, Yujin; Rothenhausler, Dominik
  作者单位：Stanford University
  摘要：Many existing approaches for estimating parameters in settings with distributional shifts operate under an invariance assumption. For example, under covariate shift, it is assumed that p(y|x) remains invariant. We refer to such distribution shifts as sparse, since they may be substantial but affect only a part of the data generating system. In contrast, in various real-world settings, shifts might be dense. More specifically, these dense distributional shifts may arise through numerous small a...
• Adaptive Selection for False Discovery Rate Control Leveraging Symmetry AI翻译
  作者：Wang, Kehan; Chen, Yuexin; Han, Yixin; Xu, Wangli; Kong, Linglong
  作者单位：Renmin University of China; Renmin University of China; University of Alberta
  摘要：Controlling the false discovery rate (FDR) in high-dimensional multiple testing has recently been advanced through mirror statistics via knockoff and data splitting. However, these approaches primarily emphasize the symmetry structure of the one-dimensional mirror statistics while inadvertently overlooking the distribution information from non-null features when determining the rejection region, potentially causing a power loss. To tackle this challenge, we present a novel framework termed sym...