Journal of the American Statistical Association

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• Selecting and Ranking Individualized Treatment Rules With Unmeasured Confounding AI翻译
  作者：Zhang, Bo; Weiss, Jordan; Small, Dylan S.; Zhao, Qingyuan
  作者单位：University of Pennsylvania; University of Pennsylvania; University of Cambridge
  摘要：It is common to compare individualized treatment rules based on the value function, which is the expected potential outcome under the treatment rule. Although the value function is not point-identified when there is unmeasured confounding, it still defines a partial order among the treatment rules under Rosenbaum's sensitivity analysis model. We first consider how to compare two treatment rules with unmeasured confounding in the single-decision setting and then use this pairwise test to rank m...
• Hierarchical Probabilistic Forecasting of Electricity Demand With Smart Meter Data AI翻译
  作者：Ben Taieb, Souhaib; Taylor, James W.; Hyndman, Rob J.
  作者单位：University of Mons; University of Oxford; Monash University
  摘要：Decisions regarding the supply of electricity across a power grid must take into consideration the inherent uncertainty in demand. Optimal decision-making requires probabilistic forecasts for demand in a hierarchy with various levels of aggregation, such as substations, cities, and regions. The forecasts should be coherent in the sense that the forecast of the aggregated series should equal the sum of the forecasts of the corresponding disaggregated series. Coherency is essential, since the al...
• Rejoinder: New Objectives for Policy Learning AI翻译
  作者：Kallus, Nathan
  作者单位：Cornell University; Cornell University
• Introduction to the Theory and Methods Special Issue on Precision Medicine and Individualized Policy Discovery AI翻译
  作者：Kosorok, Michael R.; Laber, Eric B.; Small, Dylan S.; Zeng, Donglin
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; North Carolina State University; University of Pennsylvania
  摘要：We introduce the Theory and Methods Special Issue on Precision Medicine and Individualized Policy Discovery. The issue consists of four discussion papers, grouped into two pairs, and sixteen regular research papers that cover many important lines of research on data-driven decision making. We hope that the many provocative and original ideas presented herein will inspire further work and development in precision medicine and personalization.
• Bayesian Joint Modeling of Multiple Brain Functional Networks AI翻译
  作者：Lukemire, Joshua; Kundu, Suprateek; Pagnoni, Giuseppe; Guo, Ying
  作者单位：Emory University; Universita di Modena e Reggio Emilia
  摘要：Investigating the similarity and changes in brain networks under different mental conditions has become increasingly important in neuroscience research. A standard separate estimation strategy fails to pool information across networks and hence has reduced estimation accuracy and power to detect between-network differences. Motivated by an fMRI Stroop task experiment that involves multiple related tasks, we develop an integrative Bayesian approach for jointly modeling multiple brain networks t...
• Grouped Heterogeneous Mixture Modeling for Clustered Data AI翻译
  作者：Sugasawa, Shonosuke
  作者单位：University of Tokyo
  摘要：Clustered data are ubiquitous in a variety of scientific fields. In this article, we propose a flexible and interpretable modeling approach, called grouped heterogeneous mixture modeling, for clustered data, which models cluster-wise conditional distributions by mixtures of latent conditional distributions common to all the clusters. In the model, we assume that clusters are divided into a finite number of groups and mixing proportions are the same within the same group. We provide a simple ge...
• Randomization Tests in Observational Studies With Staggered Adoption of Treatment AI翻译
  作者：Shaikh, Azeem M.; Toulis, Panos
  作者单位：University of Chicago; University of Chicago
  摘要：This article considers the problem of inference in observational studies with time-varying adoption of treatment. In addition to an unconfoundedness assumption that the potential outcomes are independent of the times at which units adopt treatment conditional on the units' observed characteristics, our analysis assumes that the time at which each unit adopts treatment follows a Cox proportional hazards model. This assumption permits the time at which each unit adopts treatment to depend on the...
• Counterfactual Analysis With Artificial Controls: Inference, High Dimensions, and Nonstationarity AI翻译
  作者：Masini, Ricardo; Medeiros, Marcelo C.
  作者单位：Getulio Vargas Foundation; Pontificia Universidade Catolica do Rio de Janeiro; Princeton University
  摘要：Recently, there has been growing interest in developing statistical tools to conduct counterfactual analysis with aggregate data when a single treated unit suffers an intervention, such as a policy change, and there is no obvious control group. Usually, the proposed methods are based on the construction of an artificial counterfactual from a pool of untre ated peers, organized in a panel data structure. In this article, we consider a general framework for counterfactual analysis for high-dimen...
• Handbook of Environmental and Ecological Statistics AI翻译
  作者：Chiu, Grace S.
  作者单位：William & Mary; Virginia Institute of Marine Science
• Gene-Based Association Testing of Dichotomous Traits With Generalized Functional Linear Mixed Models Using Extended Pedigrees: Applications to Age-Related Macular Degeneration AI翻译
  作者：Jiang, Yingda; Chiu, Chi-Yang; Yan, Qi; Chen, Wei; Gorin, Michael B.; Conley, Yvette P.; Lakhal-Chaieb, M'hamed Lajmi; Cook, Richard J.; Amos, Christopher, I; Wilson, Alexander F.; Bailey-Wilson, Joan E.; McMahon, Francis J.; Vazquez, Ana, I; Yuan, Ao; Zhong, Xiaogang; Xiong, Momiao; Weeks, Daniel E.; Fan, Ruzong
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; University of Tennessee System; University of Tennessee Health Science Center; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; University of California System; University of California Los Angeles; University of California Los Angeles Medical Center; David Geffen School of Medicine at UCLA; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Laval University; Baylor College of Medicine; National Institutes of Health (NIH) - USA; NIH National Human Genome Research Institute (NHGRI); NIH National Institute on Aging (NIA); National Institutes of Health (NIH) - USA; NIH National Institute of Mental Health (NIMH); National Institutes of Health (NIH) - USA; NIH National Institute of Mental Health (NIMH); Michigan State University; Georgetown University; University of Texas System; University of Texas Health Science Center Houston; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; National Institutes of Health (NIH) - USA; NIH Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD); Cornell University; Weill Cornell Medicine; NewYork-Presbyterian Hospital; Columbia University
  摘要：Genetics plays a role in age-related macular degeneration (AMD), a common cause of blindness in the elderly. There is a need for powerful methods for carrying out region-based association tests between a dichotomous trait like AMD and genetic variants on family data. Here, we apply our new generalized functional linear mixed models (GFLMM) developed to test for gene-based association in a set of AMD families. Using common and rare variants, we observe significant association with two known AMD...