Journal of the American Statistical Association

最新文章

• Statement of death rates. AI翻译
  作者：[Anonymous]
• Obstacles to accurate statistics. AI翻译
  作者：Blodgett, James H.
• Comparative statistics of railroad rates. AI翻译
  作者：Swain, Henry H.
• Concentration in pig-iron and coal production. AI翻译
  作者：Parker, E. W.
• Uniformity in Census returns. AI翻译
  作者：[Anonymous]
• Multilinear Ordinary Differential Equations for Inferring Gene Regulatory Network from Multimodal Functional Data AI翻译
  作者：Zhang, Heng; Liu, Zhi-Ping
  作者单位：Shandong University; Shandong University
  摘要：The acquisition of multimodal functional data, wherein functional data from multiple modalities are concurrently recorded for a single subject, has emerged as a promising strategy for constructing more accurate gene regulatory networks (GRNs). While ordinary differential equation (ODE) methods have been extensively used for modeling GRNs, the existing methods are primarily tailored for single-modal data. In this article, we introduce a multilinear ODE (ML-ODE) framework that integrates ODE-bas...
• Optimal Network-Guided Covariate Selection for High-Dimensional Data Integration AI翻译
  作者：Shen, Tao; Wang, Wanjie
  作者单位：National University of Singapore; National University of Singapore
  摘要：Modern data often arise with multiple modalities. For example, covariates and a network are observed on the same subjects, and both contain useful information. Effectively integrating these modalities is important and challenging, especially when the response is unavailable. We study the fundamental covariate selection problem for high-dimensional data by leveraging network information. We propose the Network-Guided Covariate Selection (NGCS) algorithm. NGCS exploits the spectral structure of ...
• An AI-powered Bayesian Generative Modeling Approach for Causal Inference in Observational Studies AI翻译
  作者：Liu, Qiao; Wong, Wing Hung
  作者单位：Yale University; Yale University; Stanford University
  摘要：Causal inference in observational studies with high-dimensional covariates presents significant challenges. We introduce CausalBGM, an AI-powered Bayesian generative modeling approach that captures the causal relationship among covariates, treatment, and outcome. The core innovation is to estimate the individual treatment effect (ITE) by learning the individual-specific distribution of a low-dimensional latent feature set (e.g., latent confounders) that drives changes in both treatment and out...
• Functional-SVD for Heterogeneous Trajectories: Case Studies in Health AI翻译
  作者：Tan, Jianbin; Shi, Pixu; Zhang, Anru R.
  作者单位：Duke University; Duke University
  摘要：Trajectory data, including time series and longitudinal measurements, are increasingly common in health-related domains such as biomedical research and epidemiology. Real-world trajectory data frequently exhibit heterogeneity across subjects such as patients, sites, and subpopulations, yet many traditional methods are not designed to accommodate such heterogeneity in data analysis. To address this, we propose a unified framework, termed Functional Singular Value Decomposition (FSVD), for stati...
• On the Identifying Power of Generalized Monotonicity for Average Treatment Effects AI翻译
  作者：Bai, Yuehao; Huang, Shunzhuang; Moon, Sarah; Shaikh, Azeem M.; Vytlacil, Edward J.
  作者单位：University of Southern California; University of Chicago; Massachusetts Institute of Technology (MIT); University of Chicago; Yale University
  摘要：In the context of a binary outcome, treatment, and instrument, Balke and Pearl establish that the monotonicity condition of Imbens and Angrist has no identifying power beyond instrument exogeneity for average potential outcomes and average treatment effects in the sense that adding it to instrument exogeneity does not decrease the identified sets for those parameters whenever those restrictions are consistent with the distribution of the observable data. This article shows that this phenomenon...