Journal of the American Statistical Association

最新文章

• Searching for Local Associations while Controlling the False Discovery Rate AI翻译
  作者：Gablenz, Paula; Sesia, Matteo; Sun, Tianshu; Sabatti, Chiara
  作者单位：Stanford University; University of Southern California; University of Southern California; Stanford University; Stanford Medicine; Stanford University; Stanford Medicine
  摘要：We introduce local conditional hypotheses that express how the relation between explanatory variables and outcomes changes across different contexts, described by covariates. By expanding upon the model-X knockoff filter, we show how to adaptively discover these local associations, all while controlling the false discovery rate. Our enhanced inferences can help explain sample heterogeneity and uncover interactions, making better use of the capabilities offered by modern machine learning models...
• Data-Driven Knowledge Transfer in Batch Q* Learning AI翻译
  作者：Chen, Elynn; Chen, Xi; Jing, Wenbo
  作者单位：New York University; City University of Hong Kong
  摘要：In data-driven decision-making across marketing, healthcare, and education, leveraging large datasets from existing ventures is crucial for navigating high-dimensional feature spaces and addressing data scarcity in new ventures. We investigate knowledge transfer in dynamic decision-making by focusing on batch stationary environments and formally defining task discrepancies through the framework of Markov decision processes (MDPs). We propose the Transfer Fitted Q-Iteration algorithm with gener...
• Frequency-Band Estimation of the Number of Factors AI翻译
  作者：Avarucci, Marco; Cavicchioli, Maddalena; Forni, Mario; Zaffaroni, Paolo
  作者单位：University of Glasgow; Universita di Modena e Reggio Emilia; Imperial College London; Sapienza University Rome
  摘要：We introduce consistent estimators for the number of shocks driving large-dimensional dynamic factor models. Our estimator can be applied to single frequencies and specific frequency bands, making it suitable for disentangling shocks affecting dynamic models with a factor model representation. Noticeably, our estimator requires the time-series and cross-section sizes to diverge simultaneously without any constraint and it is free of nuisance parameters, such as penalization terms. Our methodol...
• Online Auction Design Using Distribution-Free Uncertainty Quantification with Applications to E-Commerce AI翻译
  作者：Han, Jiale; Dai, Xiaowu
  作者单位：University of California System; University of California Los Angeles; University of California System; University of California Los Angeles
  摘要：Online auction is a cornerstone of e-commerce, and a key challenge is designing incentive-compatible mechanisms that maximize expected revenue. Existing approaches often assume known bidder value distributions and fixed sets of bidders and items, but these assumptions rarely hold in real-world settings where bidder values are unknown, and the number of future participants is uncertain. In this article, we introduce the Conformal Online Auction Design (COAD), a novel mechanism that maximizes re...
• Optimal Run Order for Order-of-Addition Experiments AI翻译
  作者：Wang, Chunyan; Peng, Jiayu; Lin, Dennis K. J.
  作者单位：Renmin University of China; Renmin University of China; Alphabet Inc.; Google Incorporated; Purdue University System; Purdue University
  摘要：Order-of-addition experiments have emerged as a cornerstone in modern experimental design, yet the critical role of run-order has been entirely neglected in the literature. This oversight is surprising, given that the run order can significantly influence the cost, efficiency, and validity of the experiment. Certain run orders are inherently more economical and effective, while suboptimal orders may introduce unnecessary complexities or compromise results. To ensure experimental integrity, an ...
• Cluster-Randomized Trials with Cross-Cluster Interference AI翻译
  作者：Leung, Michael P.
  作者单位：University of California System; University of California Santa Cruz
  摘要：The literature on cluster-randomized trials typically allows for interference within but not across clusters. This may be implausible when units are irregularly distributed across space without well-separated communities, as clusters in such cases may not align with significant geographic, social, or economic divisions. This article develops methods for reducing bias due to cross-cluster interference. We first propose an estimation strategy that excludes units not surrounded by clusters assign...
• Debiased Calibration Estimation Using Generalized Entropy in Survey Sampling AI翻译
  作者：Kwon, Yonghyun; Kim, Jae Kwang; Qiu, Yumou
  作者单位：Iowa State University; Peking University; Peking University
  摘要：Incorporating auxiliary information into the survey estimation is a fundamental problem in survey sampling. Calibration weighting is a widely used technique to integrate such information by adjusting design weights to meet benchmarking constraints. Traditional methods, such as those proposed by Deville and S & auml;rndal, solve this problem by minimizing a distance between calibrated and design weights. In this article, we propose a novel calibration framework that instead maximizes a generali...
• Integrative Analysis of Microbial 16S Gene and Shotgun Metagenomic Sequencing Data Improves Statistical Efficiency in Testing Differential Abundance AI翻译
  作者：Yue, Ye; Mao, Yicong; Read, Timothy D.; Fedirko, Veronika; Satten, Glen A.; Chen, Xuan; Zhan, Xiang; Hu, Yi-Juan
  作者单位：Emory University; Rollins School Public Health; Peking University; Emory University; University of Texas System; UTMD Anderson Cancer Center; Emory University; Rollins School Public Health; Emory University; Huazhong Agricultural University; Southeast University - China; Peking University; Peking University
  摘要：The most widely used technologies for profiling microbial communities are 16S marker-gene sequencing and shotgun metagenomic sequencing. Surprisingly, many microbiome studies have performed both experiments on the same cohort of samples. The two sequencing datasets often reveal consistent patterns of microbial signatures, suggesting that an integrative analysis of both datasets could enhance the testing power for these signatures. However, differential experimental biases, partially overlappin...
• Causal Inference for Genomic Data with Multiple Heterogeneous Outcomes AI翻译
  作者：Du, Jin-Hong; Zeng, Zhenghao; Kennedy, Edward H.; Wasserman, Larry; Roeder, Kathryn
  作者单位：Carnegie Mellon University; Carnegie Mellon University; Carnegie Mellon University
  摘要：With the evolution of single-cell RNA sequencing techniques into a standard approach in genomics, it has become possible to conduct cohort-level causal inferences based on single-cell-level measurements. However, the individual gene expression levels of interest are not directly observable; instead, only repeated proxy measurements from each individual's cells are available, providing a derived outcome to estimate the underlying outcome for each of many genes. In this article, we propose a gen...
• Asymptotic Behavior of Adversarial Training Estimator under ℓ∞-Perturbation AI翻译
  作者：Xie, Yiling; Huo, Xiaoming
  作者单位：University System of Georgia; Georgia Institute of Technology
  摘要：Adversarial training has been proposed to protect machine learning models against adversarial attacks. This article focuses on adversarial training under l(infinity)-perturbation, which has recently attracted much research attention. The asymptotic behavior of the adversarial training estimator is investigated in the generalized linear model. The results imply that the asymptotic distribution of the adversarial training estimator under l(infinity)-perturbation could put a positive probability ...