Journal of the American Statistical Association

最新文章

• Partnering With Authors to Enhance Reproducibility at JASA AI翻译
  作者：Wrobel, Julia; Hector, Emily C.; Crawford, Lorin; Mcgowan, Lucy D'Agostino; da Silva, Natalia; Goldsmith, Jeff; Hicks, Stephanie; Kane, Michael; Lee, Youjin; Mayrink, Vinicius; Paciorek, Christopher J.; Usher, Therri; Wolfson, Julian
  作者单位：Emory University; North Carolina State University; Microsoft; Brown University; Wake Forest University; Universidad de la Republica, Uruguay; Columbia University; Johns Hopkins University; Johns Hopkins University; Yale University; Brown University; Universidade Federal de Minas Gerais; University of California System; University of California Berkeley; US Food & Drug Administration (FDA); University of Minnesota System; University of Minnesota Twin Cities
• Policy Learning with Asymmetric Counterfactual Utilities AI翻译
  作者：Ben-Michael, Eli; Imai, Kosuke; Jiang, Zhichao
  作者单位：Carnegie Mellon University; Carnegie Mellon University; Harvard University; Sun Yat Sen University
  摘要：Data-driven decision making plays an important role even in high stakes settings like medicine and public policy. Learning optimal policies from observed data requires a careful formulation of the utility function whose expected value is maximized across a population. Although researchers typically use utilities that depend on observed outcomes alone, in many settings the decision maker's utility function is more properly characterized by the joint set of potential outcomes under all actions. ...
• Semiparametrically Efficient Method for Enveloped Central Space AI翻译
  作者：Ma, Linquan; Wang, Jixin; Chen, Han; Liu, Lan
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; University of Wisconsin System; University of Wisconsin Madison; Rice University; University of California System; University of California Davis
  摘要：The estimation of the central space is at the core of the sufficient dimension reduction (SDR) literature. However, it is well known that the finite-sample estimation suffers from collinearity among predictors. Cook, Helland, and Su proposed the predictor envelope method under linear models that can alleviate the problem by targeting a bigger space-which not only envelopes the central information, but also partitions the predictors by finding an uncorrelated set of material and immaterial pred...
• Factor Augmented Inverse Regression and its Application to Microbiome Data Analysis AI翻译
  作者：Pang, Daolin; Zhao, Hongyu; Wang, Tao
  作者单位：Shanghai Jiao Tong University; Yale University; Shanghai Jiao Tong University; Shanghai Jiao Tong University; Shanghai Jiao Tong University
  摘要：We investigate the relationship between count data that inform the relative abundance of features of a composition, and factors that influence the composition. Our work is motivated from microbiome studies aiming to extract microbial signatures that are predictive of host phenotypes based on data collected from a group of individuals harboring radically different microbial communities. We introduce multinomial Factor Augmented Inverse Regression (FAIR) of the count vector onto response factors...
• Bahadur Efficiency of Observational Block Designs AI翻译
  作者：Rosenbaum, Paul R.
  作者单位：University of Pennsylvania
  摘要：To be convincing, an observational or nonrandomized study of causal effects must demonstrate that its conclusions cannot be readily explained by a small unmeasured bias in the way individuals were assigned to treatment or control. The Bahadur relative efficiency of a sensitivity analysis compares the performance of different test statistics or different research designs when sensitivity to unmeasured bias is appraised: better statistics and better designs exhibit insensitivity to larger biases...
• Statistical Inferences for Complex Dependence of Multimodal Imaging Data AI翻译
  作者：Chang, Jinyuan; He, Jing; Kang, Jian; Wu, Mingcong
  作者单位：Southwestern University of Finance & Economics - China; Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; University of Michigan System; University of Michigan
  摘要：Statistical analysis of multimodal imaging data is a challenging task, since the data involves high-dimensionality, strong spatial correlations and complex data structures. In this article, we propose rigorous statistical testing procedures for making inferences on the complex dependence of multimodal imaging data. Motivated by the analysis of multi-task fMRI data in the Human Connectome Project (HCP) study, we particularly address three hypothesis testing problems: (a) testing independence am...
• Crowdsourcing Utilizing Subgroup Structure of Latent Factor Modeling AI翻译
  作者：Xu, Qi; Yuan, Yubai; Wang, Junhui; Qu, Annie
  作者单位：University of California System; University of California Irvine; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Chinese University of Hong Kong; University of California System; University of California Irvine
  摘要：Crowdsourcing has emerged as an alternative solution for collecting large scale labels. However, the majority of recruited workers are not domain experts, so their contributed labels could be noisy. In this article, we propose a two-stage model to predict the true labels for multicategory classification tasks in crowdsourcing. In the first stage, we fit the observed labels with a latent factor model and incorporate subgroup structures for both tasks and workers through a multi-centroid groupin...
• Latent Network Structure Learning From High-Dimensional Multivariate Point Processes AI翻译
  作者：Cai, Biao; Zhang, Jingfei; Guan, Yongtao
  作者单位：University of Miami
  摘要：Learning the latent network structure from large scale multivariate point process data is an important task in a wide range of scientific and business applications. For instance, we might wish to estimate the neuronal functional connectivity network based on spiking times recorded from a collection of neurons. To characterize the complex processes underlying the observed data, we propose a new and flexible class of nonstationary Hawkes processes that allow both excitatory and inhibitory effect...
• Sparse Independent Component Analysis with an Application to Cortical Surface fMRI Data in Autism AI翻译
  作者：Wang, Zihang; Gaynanova, Irina; Aravkin, Aleksandr; Risk, Benjamin B.
  作者单位：Emory University; University of Michigan System; University of Michigan; University of Washington; University of Washington Seattle
  摘要：Independent component analysis (ICA) is widely used to estimate spatial resting-state networks and their time courses in neuroimaging studies. It is thought that independent components correspond to sparse patterns of co-activating brain locations. Previous approaches for introducing sparsity to ICA replace the non-smooth objective function with smooth approximations, resulting in components that do not achieve exact zeros. We propose a novel Sparse ICA method that enables sparse estimation of...
• Survival Mixed Membership Blockmodel AI翻译
  作者：Song, Fangda; Chu, Jing; Ma, Shuangge; Wei, Yingying
  作者单位：The Chinese University of Hong Kong, Shenzhen; Chinese University of Hong Kong; Yale University
  摘要：Whenever we send a message via a channel such as E-mail, Facebook, WhatsApp, WeChat, or LinkedIn, we care about the response rate-the probability that our message will receive a response-and the response time-how long it will take to receive a reply. Recent studies have made considerable efforts to model the sending behaviors of messages in social networks with point processes. However, statistical research on modeling response rates and response times on social networks is still lacking. Comp...