Journal of the American Statistical Association

最新文章

• Statistical Methods in Health Disparity Research AI翻译
  作者：Paddock, Susan M.
  作者单位：University of Chicago
• Bounding Wasserstein Distance with Couplings AI翻译
  作者：Biswas, Niloy; Mackey, Lester
  作者单位：Harvard University; Microsoft
  摘要：Markov chain Monte Carlo (MCMC) provides asymptotically consistent estimates of intractable posterior expectations as the number of iterations tends to infinity. However, in large data applications, MCMC can be computationally expensive per iteration. This has catalyzed interest in approximating MCMC in a manner that improves computational speed per iteration but does not produce asymptotically consistent estimates. In this article, we propose estimators based on couplings of Markov chains to ...
• Latent Space Modeling of Hypergraph Data AI翻译
  作者：Turnbull, Kathryn; Lunagomez, Simon; Nemeth, Christopher; Airoldi, Edoardo
  作者单位：Lancaster University; Instituto Tecnologico Autonomo de Mexico; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University
  摘要：The increasing prevalence of relational data describing interactions among a target population has motivated a wide literature on statistical network analysis. In many applications, interactions may involve more than two members of the population and this data is more appropriately represented by a hypergraph. In this article, we present a model for hypergraph data that extends the well-established latent space approach for graphs and, by drawing a connection to constructs from computational t...
• A Semiparametric Inverse Reinforcement Learning Approach to Characterize Decision Making for Mental Disorders AI翻译
  作者：Guo, Xingche; Zeng, Donglin; Wang, Yuanjia
  作者单位：Columbia University; University of Michigan System; University of Michigan; Columbia University; Columbia University
  摘要：Major depressive disorder (MDD) is one of the leading causes of disability-adjusted life years. Emerging evidence indicates the presence of reward processing abnormalities in MDD. An important scientific question is whether the abnormalities are due to reduced sensitivity to received rewards or reduced learning ability. Motivated by the probabilistic reward task (PRT) experiment in the EMBARC study, we propose a semiparametric inverse reinforcement learning (RL) approach to characterize the re...
• Doubly Robust Capture-Recapture Methods for Estimating Population Size AI翻译
  作者：Das, Manjari; Kennedy, Edward H.; Jewell, Nicholas P.
  作者单位：Carnegie Mellon University; University of London; London School of Hygiene & Tropical Medicine; University of California System; University of California Berkeley; Carnegie Mellon University
  摘要：Estimation of population size using incomplete lists has a long history across many biological and social sciences. For example, human rights groups often construct partial lists of victims of armed conflicts, to estimate the total number of victims. Earlier statistical methods for this setup often use parametric assumptions, or rely on suboptimal plug-in-type nonparametric estimators; but both approaches can lead to substantial bias, the former via model misspecification and the latter via sm...
• Skeleton Clustering: Dimension-Free Density-Aided Clustering AI翻译
  作者：Wei, Zeyu; Chen, Yen-Chi
  作者单位：University of Washington; University of Washington Seattle
  摘要：We introduce a density-aided clustering method called Skeleton Clustering that can detect clusters in multivariate and even high-dimensional data with irregular shapes. To bypass the curse of dimensionality, we propose surrogate density measures that are less dependent on the dimension but have intuitive geometric interpretations. The clustering framework constructs a concise representation of the given data as an intermediate step and can be thought of as a combination of prototype methods, d...
• Selective Inference for Hierarchical Clustering AI翻译
  作者：Gao, Lucy L.; Bien, Jacob; Witten, Daniela
  作者单位：University of British Columbia; University of Southern California; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle
  摘要：Classical tests for a difference in means control the Type I error rate when the groups are defined a priori. However, when the groups are instead defined via clustering, then applying a classical test yields an extremely inflated Type I error rate. Notably, this problem persists even if two separate and independent datasets are used to define the groups and to test for a difference in their means. To address this problem, in this article, we propose a selective inference approach to test for ...
• A Decorrelating and Debiasing Approach to Simultaneous Inference for High-Dimensional Confounded Models AI翻译
  作者：Sun, Yinrui; Ma, Li; Xia, Yin
  作者单位：Fudan University
  摘要：Motivated by the simultaneous association analysis with the presence of latent confounders, this article studies the large-scale hypothesis testing problem for the high-dimensional confounded linear models with both non-asymptotic and asymptotic false discovery control. Such model covers a wide range of practical settings where both the response and the predictors may be confounded. In the presence of the high-dimensional predictors and the unobservable confounders, the simultaneous inference ...
• De-confounding Causal Inference Using Latent Multiple-Mediator Pathways AI翻译
  作者：Yuan, Yubai; Qu, Annie
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of California System; University of California Irvine
  摘要：Causal effect estimation from observational data is one of the essential problems in causal inference. However, most estimation methods rely on the strong assumption that all confounders are observed, which is impractical and untestable in the real world. We develop a mediation analysis framework inferring the latent confounder for debiasing both direct and indirect causal effects. Specifically, we introduce generalized structural equation modeling that incorporates structured latent factors t...
• Simultaneous Decorrelation of Matrix Time Series AI翻译
  作者：Han, Yuefeng; Chen, Rong; Zhang, Cun-Hui; Yao, Qiwei
  作者单位：University of Notre Dame; Rutgers University System; Rutgers University New Brunswick; University of London; London School Economics & Political Science
  摘要：We propose a contemporaneous bilinear transformation for a p x q matrix time series to alleviate the difficulties in modeling and forecasting matrix time series when p and/or q are large. The resulting transformed matrix assumes a block structure consisting of several small matrices, and those small matrix series are uncorrelated across all times. Hence, an overall parsimonious model is achieved by modeling each of those small matrix series separately without the loss of information on the lin...