Journal of the American Statistical Association

最新文章

• Nonparametric Causal Effects Based on Longitudinal Modified Treatment Policies AI翻译
  作者：Diaz, Ivan; Williams, Nicholas; Hoffman, Katherine L.; Schenck, Edward J.
  作者单位：Cornell University; Weill Cornell Medicine; Cornell University; Weill Cornell Medicine
  摘要：Most causal inference methods consider counterfactual variables under interventions that set the exposure to a fixed value. With continuous or multi-valued treatments or exposures, such counterfactuals may be of little practical interest because no feasible intervention can be implemented that would bring them about. Longitudinal modified treatment policies (LMTPs) are a recently developed nonparametric alternative that yield effects of immediate practical relevance with an interpretation in t...
• Estimating Causal Peer Influence in Homophilous Social Networks by Inferring Latent Locations AI翻译
  作者：McFowland, Edward, III; Shalizi, Cosma Rohilla
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; Carnegie Mellon University; The Santa Fe Institute
  摘要：Social influence cannot be identified from purely observational data on social networks, because such influence is generically confounded with latent homophily, that is, with a node's network partners being informative about the node's attributes and therefore its behavior. If the network grows according to either a latent community (stochastic block) model, or a continuous latent space model, then latent homophilous attributes can be consistently estimated from the global pattern of social ti...
• Partially Observed Dynamic Tensor Response Regression AI翻译
  作者：Zhou, Jie; Sun, Will Wei; Zhang, Jingfei; Li, Lexin
  作者单位：University of Miami; Purdue University System; Purdue University; University of California System; University of California Berkeley
  摘要：In modern data science, dynamic tensor data prevail in numerous applications. An important task is to characterize the relationship between dynamic tensor datasets and external covariates. However, the tensor data are often only partially observed, rendering many existing methods inapplicable. In this article, we develop a regression model with a partially observed dynamic tensor as the response and external covariates as the predictor. We introduce the low-rankness, sparsity, and fusion struc...
• Bayesian Nonparametric Common Atoms Regression for Generating Synthetic Controls in Clinical Trials AI翻译
  作者：Chandra, Noirrit Kiran; Sarkar, Abhra; de Groot, John F.; Yuan, Ying; Mueller, Peter
  作者单位：University of Texas System; University of Texas Dallas; University of Texas System; University of Texas Austin; University of California System; University of California San Francisco; University of Texas System; UTMD Anderson Cancer Center; University of Texas System; University of Texas Austin
  摘要：The availability of electronic health records (EHR) has opened opportunities to supplement increasingly expensive and difficult to carry out randomized controlled trials (RCT) with evidence from readily available real world data. In this paper, we use EHR data to construct synthetic control arms for treatment-only single arm trials. We propose a novel nonparametric Bayesian common atoms mixture model that allows us to find equivalent population strata in the EHR and the treatment arm and then ...
• Assessing the Most Vulnerable Subgroup to Type II Diabetes Associated with Statin Usage: Evidence from Electronic Health Record Data AI翻译
  作者：Guo, Xinzhou; Wei, Waverly; Liu, Molei; Cai, Tianxi; Wu, Chong; Wang, Jingshen
  作者单位：Hong Kong University of Science & Technology; University of California System; University of California Berkeley; Harvard University; Harvard T.H. Chan School of Public Health; University of Texas System; UTMD Anderson Cancer Center
  摘要：There have been increased concerns that the use of statins, one of the most commonly prescribed drugs for treating coronary artery disease, is potentially associated with the increased risk of new-onset type II diabetes (T2D). Nevertheless, to date, there is no robust evidence supporting as to whether and what kind of populations are indeed vulnerable for developing T2D after taking statins. In this case study, leveraging the biobank and electronic health record data in the Partner Health Syst...
• Modeling Time-Varying Random Objects and Dynamic Networks (vol 117, pg 2252, 2021) AI翻译
  作者：Dubey, Paromita; Muller, Hans-Georg
  作者单位：University of Southern California; University of California System; University of California Davis
• Improved Small Domain Estimation via Compromise Regression Weights AI翻译
  作者：Henderson, Nicholas C.; Varadhan, Ravi; Louis, Thomas A.
  作者单位：University of Michigan System; University of Michigan; Johns Hopkins University; Johns Hopkins Medicine; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health
  摘要：Shrinkage estimates of small domain parameters typically use a combination of a noisy direct estimate that only uses data from a specific small domain and a more stable regression estimate. When the regression model is misspecified, estimation performance for the noisier domains can suffer due to substantial shrinkage toward a poorly estimated regression surface. In this article, we introduce a new class of robust, empirically-driven regression weights that target estimation of the small domai...
• Conditional Separable Effects AI翻译
  作者：Stensrud, Mats J.; Robins, James M.; Sarveta, Aaron; Tchetgen, Eric J. Tchetgen; Young, Jessica G.
  作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard T.H. Chan School of Public Health; University of Pennsylvania; Harvard University; Harvard Medical School; Harvard Pilgrim Health Care
  摘要：Researchers are often interested in treatment effects on outcomes that are only defined conditional on posttreatment events. For example, in a study of the effect of different cancer treatments on quality of life at end of follow-up, the quality of life of individuals who die during the study is undefined. In these settings, naive contrasts of outcomes conditional on posttreatment events are not average causal effects, even in randomized experiments. Therefore, the effect in the principal stra...
• Sparse Reduced Rank Huber Regression in High Dimensions AI翻译
  作者：Tan, Kean Ming; Sun, Qiang; Witten, Daniela
  作者单位：University of Michigan System; University of Michigan; University of Toronto; University of Washington; University of Washington Seattle
  摘要：We propose a sparse reduced rank Huber regression for analyzing large and complex high-dimensional data with heavy-tailed random noise. The proposed method is based on a convex relaxation of a rank-and sparsity-constrained nonconvex optimization problem, which is then solved using a block coordinate descent and an alternating direction method of multipliers algorithm. We establish nonasymptotic estimation error bounds under both Frobenius and nuclear norms in the high-dimensional setting. This...
• Threshold Selection in Feature Screening for Error Rate Control AI翻译
  作者：Guo, Xu; Ren, Haojie; Zou, Changliang; Li, Runze
  作者单位：Beijing Normal University; Shanghai Jiao Tong University; Nankai University; Nankai University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：Hard thresholding rule is commonly adopted in feature screening procedures to screen out unimportant predictors for ultrahigh-dimensional data. However, different thresholds are required to adapt to different contexts of screening problems and an appropriate thresholding magnitude usually varies from the model and error distribution. With an ad-hoc choice, it is unclear whether all of the important predictors are selected or not, and it is very likely that the procedures would include many uni...