Journal of the American Statistical Association

最新文章

• Factor Augmented Matrix Regression AI翻译
  作者：Chen, Elynn; Fan, Jianqing; Zhu, Xiaonan
  作者单位：New York University; Princeton University
  摘要：We introduce Factor-Augmented Matrix Regression (FAMAR) to address the growing applications of matrix-variate data and their associated challenges, particularly with high-dimensionality and covariate correlations. FAMAR encompasses two key algorithms. The first is a novel non-iterative approach that efficiently estimates the factors and loadings of the matrix factor model, using techniques of pre-training, diverse projection, and block-wise averaging. The second algorithm offers an accelerated...
• Possibilistic Inferential Models: A Review AI翻译
  作者：Martin, Ryan
  作者单位：North Carolina State University
  摘要：An inferential model (IM) is a model describing the construction of provably reliable, data-driven uncertainty quantification and inference about relevant unknowns. IMs and Fisher's fiducial argument have similar objectives, but a fundamental distinction between the two is that the former doesn't require that uncertainty quantification be probabilistic, offering greater flexibility and allowing for a proof of its reliability. Important recent developments have been made thanks in part to newfo...
• Random Pairing MLE for Estimation of Item Parameters in Rasch Model AI翻译
  作者：Yang, Yuepeng; Ma, Cong
  作者单位：Yale University; University of Chicago
  摘要：The Rasch model, a classical model in the item response theory, is widely used in psychometrics to model the relationship between individuals' latent traits and their binary responses to assessments or questionnaires. In this article, we introduce a new likelihood-based estimator-random pairing maximum likelihood estimator (RP-MLE ) and its bootstrapped variant multiple random pairing MLE (MRP-MLE ) which faithfully estimate the item parameters in the Rasch model. The new estimators have sever...
• Confidence Sets for Causal Orderings AI翻译
  作者：Wang, Y. Samuel; Kolar, Mladen; Drton, Mathias
  作者单位：Cornell University; University of Southern California; Mohamed bin Zayed University of Artificial Intelligence MBZUAI; Technical University of Munich
  摘要：Causal discovery procedures aim to deduce causal relationships among variables in a multivariate dataset. While various methods have been proposed for estimating a single causal model or a single equivalence class of models, less attention has been given to quantifying uncertainty in causal discovery in terms of confidence statements. A primary challenge in causal discovery of directed acyclic graphs is determining a causal ordering among the variables, and our work offers a framework for cons...
• Estimating Racial Disparities When Race is Not Observed AI翻译
  作者：McCartan, Cory; Fisher, Robin; Goldin, Jacob; Ho, Daniel E.; Imai, Kosuke
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; United States Department of the Treasury; University of Chicago; National Bureau of Economic Research; Stanford University; Stanford University; Harvard University; Harvard University
  摘要：Estimating racial disparities without access to individual-level racial information is a common challenge in economic and policy settings. We develop a statistical method that relaxes the strong independence assumption of common race imputation approaches like Bayesian-Improved Surname Geocoding (BISG). Our identification assumption is that surname is conditionally independent of the outcome given (unobserved) race, residence location, and other observed characteristics. The proposed approach ...
• Mutually Exciting Point Processes with Latency AI翻译
  作者：Potiron, Yoann; Volkov, Vladimir
  作者单位：Keio University; University of Tasmania; HSE University (National Research University Higher School of Economics)
  摘要：A novel statistical approach to estimating latency, defined as the time it takes to learn about an event and generate response to this event, is proposed. Our approach only requires a multidimensional point process describing event times, which circumvents the use of more detailed datasets which may not even be available. We consider the class of parametric Hawkes models capturing clustering effects and define latency as a known function of kernel parameters, typically the mode of kernel funct...
• Asymptotic Guarantees for Bayesian Phylogenetic Tree Reconstruction AI翻译
  作者：Kirichenko, Alisa; Kelly, Luke J.; Koskela, Jere
  作者单位：University of Warwick; University College Cork; Newcastle University - UK
  摘要：We derive tractable criteria for the consistency of Bayesian tree reconstruction procedures, which constitute a central class of algorithms for inferring common ancestry among DNA sequence samples in phylogenetics. Our results encompass several Bayesian algorithms in widespread use, such as BEAST, MrBayes, and RevBayes. Unlike essentially all existing asymptotic guarantees for tree reconstruction, we require no discretization or boundedness assumptions on branch lengths. Our results are also v...
• Bayesian Inference on Brain-Computer Interfaces via GLASS AI翻译
  作者：Zhao, Bangyao; Huggins, Jane E.; Kang, Jian
  作者单位：University of Michigan System; University of Michigan; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
  摘要：Brain-computer interfaces (BCIs), particularly the P300 BCI, facilitate direct communication between the brain and computers. The fundamental statistical problem in P300 BCIs lies in classifying target and non-target stimuli based on electroencephalogram (EEG) signals. However, the low signal-to-noise ratio (SNR) and complex spatial/temporal correlations of EEG signals present challenges in modeling and computation, especially for individuals with severe physical disabilities-BCI's primary use...
• GeoWarp: Warped Spatial Processes for Inferring Subsea Sediment Properties AI翻译
  作者：Bertolacci, Michael; Zammit-Mangion, Andrew; Giraldo, Juan Valderrama; O'Neill, Michael; Bransby, Fraser; Watson, Phil
  作者单位：University of Western Australia; University of Wollongong; University of Western Australia
  摘要：For offshore structures like wind turbines, subsea infrastructure, pipelines, and cables, it is crucial to quantify the properties of the seabed sediments at a proposed site. However, data collection offshore is costly, so analysis of the seabed sediments must be made from measurements that are spatially sparse. Adding to this challenge, the structure of the seabed sediments exhibits both nonstationarity and anisotropy. To address these issues, we propose GeoWarp, a hierarchical spatial statis...
• Deconfounding via Profiled Transfer Learning AI翻译
  作者：Chen, Ziyuan; Jiang, Yifan; Liu, Jingyuan; Yao, Fang
  作者单位：Peking University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Xiamen University
  摘要：Unmeasured confounders are a major source of bias in regression-based effect estimation and causal inference. In this article, we propose a new profiled transfer learning framework, ProTrans, to address confounding effects in the target dataset, when additional source datasets with similar confounding structures are available. We introduce the concept of profiled residuals to characterize the shared confounding patterns between source and target datasets. By incorporating these profiled residu...