Journal of the American Statistical Association

最新文章

• Bayesian Inference for Sequential Treatments Under Latent Sequential Ignorability AI翻译
  作者：Ricciardi, Federico; Mattei, Alessandra; Mealli, Fabrizia
  作者单位：University of London; University College London; University of Florence
  摘要：We focus on causal inference for longitudinal treatments, where units are assigned to treatments at multiple time points, aiming to assess the effect of different treatment sequences on an outcome observed at a final point. A common assumption in similar studies is sequential ignorability (SI): treatment assignment at each time point is assumed independent of future potential outcomes given past observed outcomes and covariates. SI is questionable when treatment participation depends on indivi...
• A Semiparametric Instrumental Variable Approach to Optimal Treatment Regimes Under Endogeneity AI翻译
  作者：Cui, Yifan; Tchetgen Tchetgen, Eric
  作者单位：University of Pennsylvania
  摘要：There is a fast-growing literature on estimating optimal treatment regimes based on randomized trials or observational studies under a key identifying condition of no unmeasured confounding. Because confounding by unmeasured factors cannot generally be ruled out with certainty in observational studies or randomized trials subject to noncompliance, we propose a general instrumental variable (IV) approach to learning optimal treatment regimes under endogeneity. Specifically, we establish identif...
• A Multi-resolution Theory for Approximating Infinite-p-Zero-n: Transitional Inference, Individualized Predictions, and a World Without Bias-Variance Tradeoff AI翻译
  作者：Li, Xinran; Meng, Xiao-Li
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; Harvard University
  摘要：Transitional inference is an empiricism concept, rooted and practiced in clinical medicine since ancient Greece. Knowledge and experiences gained from treating one entity (e.g., a disease or a group of patients) are applied to treat a related but distinctively different one (e.g., a similar disease or a new patient). This notion of transition to the similar renders individualized treatments an operational meaning, yet its theoretical foundation defies the familiar inductive inference framework...
• Model-Based Geostatistics for Global Public Health: Methods and Applications AI翻译
  作者：Laga, Ian; Niu, Xiaoyue
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
• Kernel Meets Sieve: Post-Regularization Confidence Bands for Sparse Additive Model AI翻译
  作者：Lu, Junwei; Kolar, Mladen; Liu, Han
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; University of Chicago; Northwestern University
  摘要：We develop a novel procedure for constructing confidence bands for components of a sparse additive model. Our procedure is based on a new kernel-sieve hybrid estimator that combines two most popular nonparametric estimation methods in the literature, the kernel regression and the spline method, and is of interest in its own right. Existing methods for fitting sparse additive model are primarily based on sieve estimators, while the literature on confidence bands for nonparametric models are pri...
• iFusion: Individualized Fusion Learning AI翻译
  作者：Shen, Jieli; Liu, Regina Y.; Xie, Min-ge
  作者单位：Rutgers University System; Rutgers University New Brunswick
  摘要：Inferences from different data sources can often be fused together, a process referred to as fusion learning, to yield more powerful findings than those from individual data sources alone. Effective fusion learning approaches are in growing demand as increasing number of data sources have become easily available in this big data era. This article proposes a new fusion learning approach, called iFusion, for drawing efficient individualized inference by fusing learnings from relevant data source...
• Testing and Estimation of Social Network Dependence With Time to Event Data AI翻译
  作者：Su, Lin; Lu, Wenbin; Song, Rui; Huang, Danyang
  作者单位：North Carolina State University
  摘要：Nowadays, events are spread rapidly along social networks. We are interested in whether people's responses to an event are affected by their friends' characteristics. For example, how soon will a person start playing a game given that his/her friends like it? Studying social network dependence is an emerging research area. In this work, we propose a novel latent spatial autocorrelation Cox model to study social network dependence with time-to-event data. The proposed model introduces a latent ...
• Comment on A Tuning-Free Robust and Efficient Approach to High-Dimensional Regression AI翻译
  作者：Loh, Po-Ling
  作者单位：University of Wisconsin System; University of Wisconsin Madison
• Statistical Modelling by Exponential Families AI翻译
  作者：Chen, Yen-Chi
  作者单位：University of Washington; University of Washington Seattle
• Bayesian Double Feature Allocation for Phenotyping With Electronic Health Records AI翻译
  作者：Ni, Yang; Mueller, Peter; Ji, Yuan
  作者单位：Texas A&M University System; Texas A&M University College Station; University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin; University of Chicago
  摘要：Electronic health records (EHR) provide opportunities for deeper understanding of human phenotypes-in our case, latent disease-based on statistical modeling. We propose a categorical matrix factorization method to infer latent diseases from EHR data. A latent disease is defined as an unknown biological aberration that causes a set of common symptoms for a group of patients. The proposed approach is based on a novel double feature allocation model which simultaneously allocates features to the ...